CN104463795B - A kind of dot matrix DM image in 2 D code processing method and processing device - Google Patents

Abstract

The present invention relates to a dot-matrix DM two-dimensional code image processing method and device, the method comprising: step 1, read the image and unify the size; step 2, convert it into a grayscale image; step 3, Gaussian smoothing filter processing; step Four, binarization processing; step five, spot detection obtains the code element diameter; step six, carries out dynamic filtering and improved binarization processing to the original gray scale image according to the code element diameter; step seven, carries out development to the binarized image , closing operation to obtain a dot matrix image; Step 8, the dot matrix image is converted into a standard two-dimensional code image by median filtering. This method can overcome the problems of too large gap, uneven illumination and noise interference in the recognition of dot-matrix DataMatrix codes. It can be detected by the current DM code handheld recognition equipment. There is no need for secondary development on the hardware, and the system design is feasible, fast and effective. It can meet the actual demand for dot-matrix DM decoding at present.

Description

A dot-matrix DM two-dimensional code image processing method and device

technical field

The invention belongs to the technical field of two-dimensional code computer image processing, and in particular relates to a dot matrix DM two-dimensional code image processing method and device.

Background technique

Dot-matrix DM (DataMatrix) is currently mainly used in automobile manufacturing, pharmaceutical medical treatment, military firearms management and other fields. Because it is easy to generate, it has been widely used in metal, glass, hard plastic and other materials. At the same time, due to the diversity of dot-matrix DM two-dimensional code generation methods and materials used, the barcode images generally have low contrast, multiple noise interference, complex background, and uneven illumination during the acquisition process. Different from standard DM symbols, dot-matrix DM two-dimensional codes have large spaces between dots, as shown in Figure 1. If the code is recognized without any treatment, it will increase the difficulty of recognition, so it is necessary to perform image preprocessing on the dot matrix two-dimensional code image.

With the development and promotion of embedded platforms, portable two-dimensional code recognition readers have appeared, making the reading of two-dimensional codes faster and more convenient. At present, there are endless researches on DM code image preprocessing, but for dot matrix DM There are relatively few studies on code image preprocessing. Therefore, the present invention proposes a dot-matrix DM image preprocessing method and a corresponding device.

Contents of the invention

The present invention aims at the problem of low recognition rate of dot-matrix DataMatrix two-dimensional code, proposes a series of image preprocessing methods combined with binarized morphological transformation, and makes smooth blur and morphological transformation self-adaptive through speckle detection sex. This method can overcome the problems of too large gap, uneven illumination and noise interference in the recognition of the dot matrix DataMatrix code, and convert the dot matrix DM two-dimensional code into a standard format, so that it can be recognized by the current DM code handheld identification device For detection, there is no need for secondary development on the hardware, and the system design is feasible, fast and effective, and can meet the current actual demand for dot-matrix DM decoding.

The present invention provides a dot-matrix DM two-dimensional code image processing method, comprising:

Step 1: Read the dot-matrix DM two-dimensional code image, and use the nearest neighbor interpolation algorithm or bilinear interpolation algorithm to unify the width without changing the width-to-height ratio of the original dot-matrix DM two-dimensional code image;

Step 2: convert the uniformly sized image into a grayscale image;

Step 3: Carry out Gaussian smoothing filter processing to the image after gray scale, remove the tiny texture of image background, make the solid point of lattice code element smoother;

Step 4: Convert the grayscale image after Gaussian smoothing filter into a black and white binary image;

Step 5: Carry out symbol detection to the binarized image in step 4 to obtain the diameter of the dot matrix symbol;

Step 6: Dynamically change the size of the average template according to the diameter of the dot matrix symbol obtained in step 5, and then perform dynamic mean value filtering on the grayscale image in step 2, and then perform dynamic mean value filtering on the grayscale image after dynamic mean value filtering Improved binarization processing combining kittler algorithm and Bernsen algorithm;

Step 7: Perform morphological opening and closing operations on the binarized image obtained in step 6 to obtain a processed bitmap image, wherein,

The opening operation is represented by the following formula:

The closing operation is represented by the following formula:

Among them, A is the input binary image, and B is the square structure element.

Step 8: Convert the dot matrix image obtained in Step 7 into a standard DM two-dimensional code image with recognizable block structure through median filtering and denoising processing.

Further, the side length of the square structural element B in the seven-open operation of the step is preferably 1/3 of the diameter of the lattice symbol, and the side length of the square structural element B in the closed operation is preferably less than the diameter of the lattice symbol by 1 to 2 5 display pixels.

Further, the median filter denoising process used in step 8 is preferably implemented through the following steps:

The value of a point in the image is replaced by the median value of each point value in a neighborhood of the point, so that the surrounding pixel values are close to the real value, thereby eliminating isolated noise points, and the output of the two-dimensional median filter is obtained by the following formula:

g(x,y)=med{f(x-k,y-1),(k,1∈W)}

Among them, med{} means to take the intermediate value of the array sequence; f(x, y), g(x, y) are the original image and the processed image respectively; W is a two-dimensional template with a size of 3×3; is an integer, representing the increment in the coordinate x and y directions respectively.

Further, the dynamic mean filtering process in step 6 preferably further includes the following steps:

(1) The template size is obtained by the following formula:

Among them, round means rounding operation;

(2) Calculate the average filter template:

Wherein, k is the size of the average template, D is the diameter of the lattice symbol, and ε is the average template;

(3) Perform average filtering according to the following formula:

I ₁ (x, y) = ε*I(x, y)

Wherein I(x, y) represents the grayscale matrix, and I ₁ (x, y) represents the grayscale matrix of the filtered image;

Further, the process of converting the grayscale image after Gaussian smoothing filtering into a black and white binarized image in step 4 specifically includes:

(1) Use the Kittler algorithm to obtain the global threshold T, and the method for calculating the global threshold T is as follows:

Among them, f(x, y) is the original grayscale image obtained in step 2, e(x, y)=max{|e _x |, |e _y |} is the maximum value of the gradient, e _x =f(x- 1, y)-f(x+1, y) is the gradient on the horizontal direction, e _y =f(x, y-1)-f(x, y+1) is the gradient on the vertical direction;

(2) Scan the entire f(x, y) grayscale image, then the binarization result is:

Where b(x, y) is the binarization result.

Further, the improved binarization process combining the kittler algorithm and the Bernsen algorithm used in step 6 includes the following specific steps:

Step (1) Use the Kittler algorithm to obtain the global threshold T, and the method for calculating the global threshold T is as follows:

Among them, f(x, y) is the original grayscale image obtained in step 2, e(x, y)=max{|e _x |, |e _y |} is the maximum value of the gradient, e _x =f(x- 1, y)-f(x+1, y) is the gradient on the horizontal direction, e _y =f(x, y-1)-f(x, y+1) is the gradient on the vertical direction;

Step (2) Use the Bernsen algorithm to process the interval of uneven illumination in the image histogram. The gray value of this interval is concentrated near the global threshold T obtained in step (1). If T ₃ >D, D is processed by the Bernsen algorithm The interval width is the diameter of the lattice symbol

Then the binarization result

If T ₃ <D, D is the interval width processed by the Bernsen algorithm, that is, the diameter of the lattice symbol

Then the binarization result

in,

T ₃ is the threshold selection basis and T ₃ (x, y)=max _s -min _s ,

T ₂ (x,y)=0.5(max _d +min _d ),

T ₄ (x,y)=0.5(T+T ₂ (x,y));

in,

max _d indicates the maximum pixel value of a pixel point (x, y) in a window with a size of 4w*4w;

Indicates the minimum pixel value of a pixel point (x, y) in a larger window with a size of 4w*4w;

Indicates the maximum pixel value of a pixel point (x, y) in a smaller window with a size of 2w*2w;

Indicates the minimum pixel value of a pixel point (x, y) in a smaller window with a size of 2w*2w;

In the above formula, max means to take the maximum value of the pixels in the window, min means to take the minimum value of the pixels in the window, k and l are both integers, and represent the increments in the coordinate x and y directions respectively; w represents the window of the local threshold operation , w ranges from 5 to 9, T ₂ represents the average gray level of pixels in the window, and T ₄ is the average value of T ₂ and the global threshold T.

Further, the specific process of code element detection in step five is as follows:

Use Gaussian Laplacian operator to detect image symbols, for two-dimensional Gaussian function:

Among them, σ is the width parameter of the function, that is, the characteristic scale, which is used to control the radial range of the function. The larger the σ, the wider the radial direction of the function, and the larger the similar code elements. The smaller σ, the wider the radial direction of the function. Narrow, its similar symbol is smaller, x, y represent two-dimensional space position, g (x, y, σ) represents two-dimensional Gaussian function;

The Laplace transform of formula (1) is:

Among them, Δ ² g represents the Laplace function of the two-dimensional Gaussian function, Δ ² represents the second-order differential operator, and g represents the two-dimensional Gaussian function; the normalized Gaussian Laplace transform is:

In formula (3) Represents the normalized Gaussian Laplacian function with a variance of 0;

The normalized two-dimensional Gaussian Laplace function shown in formula (3) is a circular symmetric function, by changing the value of σ, detecting two-dimensional code elements of different sizes, and obtaining Obtaining the pole value is equivalent to obtaining the following formula:

in, Denotes the normalized Laplacian of Gaussian function Find the partial derivative of σ, Represents the normalized Laplacian of Gaussian function extreme value of

that is:

r ² −2σ ² =0

Where r represents the radius of the circular symbol of the two-dimensional code image binarization, in the scale When , the Laplace of Gaussian response value reaches the maximum. Similarly, if the circular symbol in the image is black and white, then the Laplace of Gaussian response value of the symbol is in the scale of The scale σ value when the Gaussian Laplacian response reaches the peak value is the characteristic scale of symbol detection, calculate the discrete Laplacian response values of the binarized image at different scales, and then check the position space For each point of , if the Laplace response value of this point is greater than or less than the value of other cubic space neighbors, then this point is the detected two-dimensional code data element point, and the above-mentioned search position space and scale space The peak value of can be represented by the following function:

This function represents the value of the point where the Laplace response reaches the maximum or minimum value at the same time in the spatial position and scale, and this point is the symbol to be detected; where, t represents the scale value, and (x, y) represents the space position, max minlocal _{(x, y, t)} ( ) indicates the maximum or minimum value of the response function in space and scale position, arg ( ) indicates the value of the variable corresponding to the function value, Represents the standard two-dimensional Laplace function in scale space.

The present invention also provides a dot-matrix DM two-dimensional code image processing device, comprising:

The image reading module is used to read the dot-matrix DM two-dimensional code image. On the basis of not changing the width-to-height ratio of the original dot-matrix DM two-dimensional code image, the width is unified by using the nearest neighbor interpolation algorithm or bilinear interpolation algorithm treatment;

A grayscale conversion module, used to convert the image of uniform size into a grayscale image;

The Gaussian smoothing filter module is used to carry out Gaussian smoothing filter processing to the image after the gray scale, removes the small texture of the image background, and makes the solid point of the lattice code element smoother;

The binarization conversion module is used to convert the grayscale image after Gaussian smoothing filter into a black and white binarized image;

The code element detection module is used to carry out code element detection to the binarized image in the binarization conversion module to obtain the diameter of the dot matrix code element;

The dynamic mean filtering and binarization conversion module is used to dynamically change the size of the average template according to the diameter of the lattice symbol obtained by the symbol detection module, and then perform dynamic mean filtering on the grayscale image obtained by the grayscale conversion module Processing, the grayscale image after the dynamic mean filter is then subjected to an improved binarization process combining the kittler algorithm and the Bernsen algorithm;

The operation operation module is used to perform morphological opening and closing operations on the binarized image obtained by the dynamic mean filtering and binarization conversion module to obtain a processed bitmap image, wherein,

The opening operation is represented by the following formula:

The closing operation is represented by the following formula:

Where A is the input binary image; B is the square structure element;

The standardization module is used to convert the dot matrix image obtained by the operation module into a standard DM two-dimensional code image with a recognizable block structure through median filtering and denoising processing.

Further, the side length of the square structural element B in the opening operation of the operation module is preferably 1/3 of the diameter of the lattice symbol, and the side length of the square structural element B in the closing operation is preferably less than the diameter of the lattice symbol 1 to 5 display pixels.

Further, the median filter denoising process used by the standardization module is preferably implemented in the following manner:

The value of a point in the image is replaced by the median value of each point value in a neighborhood of the point, so that the surrounding pixel values are close to the real value, thereby eliminating isolated noise points, and the output of the two-dimensional median filter is obtained by the following formula:

g(x,y)=med{f(x-k,y-1),(k,1∈W)}

Among them, med{} means to take the intermediate value of the array sequence; f(x, y), g(x, y) are the original image and the processed image respectively; W is a two-dimensional template with a size of 3×3; is an integer, representing the increment in the coordinate x and y directions respectively.

Description of drawings

Fig. 1 is a comparison diagram of a dot matrix DM code and a standard DM code;

Fig. 2 is a flow chart of dot matrix DM code image processing;

Fig. 3 is the speckle (symbol) detection schematic diagram after the binarized lattice DM code image and the binarization;

Fig. 4 is the rendering of opening operation and closing operation;

Fig. 5 is a schematic diagram of the effect of converting a dot matrix DM code image into a standard DM two-dimensional code image.

detailed description

The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments, and the implementation scope of the present invention is not limited thereto.

As shown in Figure 1, the lattice DM code (right) is significantly different from the standard DM code (left).

Since the dot-matrix DM code is composed of uniform dots according to certain rules, if the image is directly blurred and smoothed and morphologically changed according to the conventional method, the effect is very unsatisfactory, and it is easy to cause over-processing and the processing effect is not obvious.

[Example 1]

This embodiment provides a dot-matrix DM two-dimensional code image processing flow and method, which can be realized by computer programs or hardware circuits, and the specific flow is shown in FIG. 2 .

(1) Read the image and unify the size

For the convenience of processing, firstly, after reading the image, the size of the dot-matrix two-dimensional image is unified, and the width is unified by using the nearest neighbor interpolation algorithm or bilinear interpolation algorithm without changing the width and height ratio of the original image. Experiments show that unification can make identification more accurate.

(2) Convert to grayscale image

It is convenient to process the image, and it is necessary to convert the original collected image into a grayscale image.

(3) Gaussian smoothing filter processing

Because the roughness of the barcode background affects the recognition accuracy, and the dot matrix modules of the dot matrix DM code are all solid spots, smoothing has little effect on the solid dots. Therefore, in order to remove the complex texture of the picture, it is necessary to perform blurring and smoothing processing. Dot-matrix DM barcodes do not require too much smoothing. Through the experimental comparison of various smoothing and filtering transformations, it is found that both dynamic mean filtering and Gaussian filtering meet the requirements. Therefore, Gaussian smoothing is used in this step to obtain a natural smoothing effect, which will not be too blurred, so that the measurement of speckle detection is more accurate and stable.

(4) Binary processing

Image binarization is to convert the grayscale image into a black and white binarized image that can reflect the overall structure and local features of the image by selecting an appropriate threshold. In the process of dot matrix Data Matrix barcode image processing, by performing corresponding operations on the binarized image, it is relatively easy to obtain characteristic information such as the boundary, position, and size of the target area, thereby laying the foundation for the analysis and recognition of barcode images .

First, use the Kittler algorithm to obtain the global threshold T, and calculate the global threshold T as follows:

Among them, f(x, y) is the original grayscale image obtained in step 2, e(x, y)=max{|e _x |, |e _y |} is the maximum value of the gradient, e _x =f(x- 1, y)-f(x+1, y) is the gradient on the horizontal direction, e _y =f(x, y-1)-f(x, y+1) is the gradient on the vertical direction;

Then, scan the entire f(x, y) grayscale image, then the binarization result is:

Where b(x, y) is the binarization result.

(5) Spot detection

The principle of speckle detection (i.e. dot matrix symbol detection) is as follows:

Utilize Laplace of Guassian (Laplace of Guassian, LoG) operator to detect image symbol, for two-dimensional Gaussian function:

Among them, σ is the width parameter of the function, that is, the characteristic scale, which is used to control the radial range of the function. The larger the σ, the wider the radial direction of the function, and the larger the similar code elements. The smaller σ, the wider the radial direction of the function. Narrow, its similar symbol is smaller, x, y represent two-dimensional space position, g (x, y, σ) represents two-dimensional Gaussian function;

The Laplace transform of formula (1) is:

Wherein, Δ ² g represents the Laplace function of the two-dimensional Gaussian function, Δ ² represents the second-order differential operator, and g represents the two-dimensional Gaussian function;

The normalized Laplace of Gaussian transform is:

In formula (3) Represents the normalized Gaussian Laplacian function with a variance of 0;

The normalized two-dimensional Gaussian Laplace function shown in formula (3) is a circular symmetric function, by changing the value of σ, detecting two-dimensional code elements of different sizes, and obtaining Obtaining the pole value is equivalent to obtaining the following formula:

in, Denotes the normalized Laplacian of Gaussian function Find the partial derivative of σ, Represents the normalized Laplacian of Gaussian function extreme value of

that is:

r ² −2σ ² =0

Where r represents the radius of the circular symbol of the two-dimensional code image binarization, in the scale When , the Laplace of Gaussian response value reaches the maximum. Similarly, if the circular symbol in the image is black and white, then the Laplace of Gaussian response value of the symbol is in the scale of The scale σ value when the Gaussian Laplacian response reaches the peak value is the characteristic scale of symbol detection, calculate the discrete Laplacian response values of the binarized image at different scales, and then check the position space For each point of , if the Laplace response value of this point is greater than or less than the value of other cubic space neighbors, then this point is the detected two-dimensional code data element point, and the above-mentioned search position space and scale space The peak value of can be represented by the following function:

This function represents the value of the point where the Laplace response reaches the maximum or minimum value at the same time in the spatial position and scale, and this point is the symbol to be detected; where, t represents the scale value, and (x, y) represents the space position, max minlocal _{(x, y, t)} ( ) indicates the maximum or minimum value of the response function in space and scale position, arg ( ) indicates the value of the variable corresponding to the function value, Represents the standard two-dimensional Laplace function in scale space.

According to the above principles, the data element points of the two-dimensional code image and the size of each spot can be well detected, as shown in FIG. 3 .

(6) Dynamic filtering and improved binarization processing

First, perform dynamic filtering processing:

(1) The template size is obtained by the following formula:

Among them, round means rounding operation;

(2) Calculate the average filter template:

Wherein, k is the size of the average template, D is the diameter of the lattice symbol, and ε is the average template;

(3) Perform average filtering according to the following formula:

I ₁ (x, y) = ε*I(x, y)

Wherein I(x, y) represents the grayscale matrix, and I ₁ (x, y) represents the grayscale matrix of the filtered image;

Then, an improved binarization is performed:

The binarization algorithm of this method selects the binarization algorithm that combines the Kittler algorithm and the improved Bernsen algorithm. For the characteristics of the dot matrix two-dimensional code point module being relatively smooth and similar to the background texture, it can be well ignored. Necessary background detail, while handling images with uneven lighting. First, according to Kittler's simple statistical algorithm, find the area where the image has uneven illumination, and then improve the processing process of Bernsen algorithm, adjust the parameters, weaken the artifact problem of the original algorithm, and use the improved algorithm to deal with the uneven illumination of the image. The algorithm has good stability and adaptability, and can significantly improve the binarization effect and recognition rate of two-dimensional barcodes.

The binarization processing method combining Kittler algorithm and improved Bernsen algorithm is as follows:

Step (1) Use the Kittler algorithm to obtain the global threshold T, and the method for calculating the global threshold T is as follows:

Among them, f(x, y) is the original grayscale image obtained in step 2, e(x, y)=max{|e _x |, |e _y |} is the maximum value of the gradient, e _x =f(x- 1, y)-f(x+1, y) is the gradient on the horizontal direction, e _y =f(x, y-1)-f(x, y+1) is the gradient on the vertical direction;

Step (2) uses the Bernsen algorithm to process the interval of uneven illumination in the image histogram, and the gray value of this type of interval is concentrated near the global threshold T obtained in step (1).

If T ₃ >D, D is the interval width processed by the Bernsen algorithm, that is, the diameter of the lattice symbol

Then the binarization result

If T ₃ <D, D is the interval width processed by the Bernsen algorithm, that is, the diameter of the lattice symbol

Then the binarization result

in,

T ₃ is the threshold selection basis and T ₃ (x, y)=max _s -min _s ,

T ₂ (x,y)=0.5(max _d +min _d ),

T ₄ (x,y)=0.5(T+T ₂ (x,y));

in,

max _d indicates the maximum pixel value of a pixel point (x, y) in a window with a size of 4w*4w;

Indicates the minimum pixel value of a pixel point (x, y) in a larger window with a size of 4w*4w;

Indicates the maximum pixel value of a pixel point (x, y) in a smaller window with a size of 2w*2w;

Indicates the minimum pixel value of a pixel point (x, y) in a smaller window with a size of 2w*2w;

In the above formula, max means to take the maximum value of the pixels in the window, min means to take the minimum value of the pixels in the window, k and l are both integers, and represent the increments in the coordinate x and y directions respectively; w represents the window of the local threshold operation , w ranges from 5 to 9, T ₂ represents the average gray level of pixels in the window, and T ₄ is the average value of T ₂ and the global threshold T.

(7) Perform opening and closing operations to obtain a dot matrix image

The dot matrix DM code is composed of point modules. In order to become a recognizable standard DM code, the dot matrix module needs to be changed into a block structure, which uses the opening and closing operations on the morphological transformation. The opening and closing operations are A combination of dilation and erosion.

Among them, A is expanded by B, denoted as defined as:

Among them, A is corroded by B, denoted as AΘB, defined as:

AΘB＝{z|(B) _z ∩A ^c ≠Φ}

The morphological opening operation of A by B can be recorded as AοB. This operation is the result of A being corroded by B and then dilated and corroded by B:

The morphological closing operation of A by B can be recorded as A·B. This operation is the result of A being expanded by B and then corroded and expanded by B:

In this method, A is the input binary image, and B is the square structure element.

The opening operation can smooth the outline of the image, and can also disconnect narrow connections and eliminate burrs. However, unlike corrosion, the large outline of the image does not shrink as a whole, and the position of the object does not change. The closing operation can also smooth the contour, but in contrast to the opening operation, it is usually able to bridge narrow gaps and fill small holes. Figure 4 is a schematic diagram of the opening operation and the closing operation. Therefore, applying the opening operation and closing operation to the standardization of the DM two-dimensional code can remove the stray points and some glitches after binarization. It can be concluded from a large number of experiments that the opening operation is performed on square structural elements that are one-third the size of the spots (ie, the diameter of the lattice symbol) obtained by spot detection to remove stray points and burrs. The closed operation is performed with a square structure element slightly smaller than the spot size (that is, the diameter of the dot matrix symbol) of 1 to 5 display pixels, so that the dot module becomes an approximate block structure, and its "L" shaped boundary is clearly visible.

(8) Perform median filter processing after opening and closing operations, and convert it into a standard two-dimensional code image

The median filter denoising process is preferably implemented in the following manner:

The value of a point in the image is replaced by the median value of each point value in a neighborhood of the point, so that the surrounding pixel values are close to the real value, thereby eliminating isolated noise points, and the output of the two-dimensional median filter is obtained by the following formula:

g(x,y)=med{f(x-k,y-1),(k,l∈W)}

Among them, med{} means to take the intermediate value of the array sequence; f(x, y), g(x, y) are the original image and the processed image respectively; W is a two-dimensional template with a size of 3×3; k, 1 mean is an integer, representing the increment in the coordinate x and y directions respectively.

The effect after converting to a standard DM two-dimensional code image is shown in Figure 5.

[Example 2]

The present invention also provides a software virtual device with a functional module architecture, which includes the following structure:

A dot-matrix DM two-dimensional code image processing device, comprising:

The image reading module is used to read the dot-matrix DM two-dimensional code image. On the basis of not changing the width-to-height ratio of the original dot-matrix DM two-dimensional code image, the width is unified by using the nearest neighbor interpolation algorithm or bilinear interpolation algorithm treatment;

A grayscale conversion module, used to convert the image of uniform size into a grayscale image;

The Gaussian smoothing filter module is used to carry out Gaussian smoothing filter processing to the image after the gray scale, removes the small texture of the image background, and makes the solid point of the lattice code element smoother;

The binarization conversion module is used to convert the grayscale image after Gaussian smoothing filter into a black and white binarized image;

The code element detection module is used to carry out code element detection to the binarized image in the binarization conversion module to obtain the diameter of the dot matrix code element;

The dynamic mean filtering and binarization conversion module is used to dynamically change the size of the average template according to the diameter of the lattice symbol obtained by the symbol detection module, and then perform dynamic mean filtering on the grayscale image obtained by the grayscale conversion module Processing, the grayscale image after the dynamic mean filter is then subjected to an improved binarization process combining the kittler algorithm and the Bernsen algorithm:

The operation operation module is used to perform morphological opening and closing operations on the binarized image obtained by the dynamic mean filtering and binarization conversion module to obtain a processed bitmap image, wherein,

The opening operation is represented by the following formula:

The closing operation is represented by the following formula:

Where A is the input binary image; B is the square structure element;

The standardization module is used to transform the dot matrix image obtained by the operation module into a standard DM two-dimensional code image with a recognizable block structure through median filtering and denoising processing.

Of course, the products with the above-mentioned functional module architecture can also be realized through real hardware circuits.

The lattice spots mentioned in the present invention are also called lattice symbols, and the spots and symbols belong to the same concept.

Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

Claims (2)

1. A dot matrix DM two-dimensional code image processing method comprises the following steps:

the method comprises the following steps: reading a dot matrix DM two-dimensional code image, and performing width unification processing by using a nearest neighbor interpolation algorithm or a bilinear interpolation algorithm on the basis of not changing the high width proportion of the original dot matrix DM two-dimensional code image;

step two: converting the images with uniform sizes into gray level images;

step three: performing Gaussian smoothing filtering processing on the grayed image to remove fine textures of an image background, so that solid points of the dot matrix code elements are smoother;

step four: converting the gray level image after Gaussian smooth filtering into a black and white binary image, and specifically comprising the following steps:

(1) obtaining a global threshold T by using a Kittler algorithm, wherein the method for calculating the global threshold T comprises the following steps:

$T=\frac{\underset{x}{\mathrm{\&Sigma;}}\underset{y}{\mathrm{\&Sigma;}}e(x,y)f(x,y)}{\underset{x}{\mathrm{\&Sigma;}}\underset{y}{\mathrm{\&Sigma;}}e(x,y)}$

wherein f (x, y) is the original gray scale map obtained in step two, and e (x, y) is max { | e_x|，|e_yI.e. the maximum value of the gradient, e_xF (x-1, y) -f (x +1, y) is the gradient in the horizontal direction, e_yF (x, y-1) -f (x, y +1) is the gradient in the vertical direction;

(2) scanning the whole f (x, y) gray level image, the binarization result is:

$b(x,y)=\left\{\begin{array}{cc}255& f(x,y)>T\\ 0& f(x,y)<T\end{array}\right.$

wherein b (x, y) is a binarization result;

step five: and (3) carrying out code element detection on the image subjected to binarization in the fourth step to obtain the diameter of the dot matrix code element, wherein the specific flow of the code element detection is as follows:

detecting the image elements by using a Gaussian Laplacian operator, and for a two-dimensional Gaussian function:

$g(x,y,\mathrm{\&sigma;})=\frac{1}{2\mathrm{\&pi;}\mathrm{\&sigma;}}{e}^{-\frac{(x^2+y^2)}{2\mathrm{\&sigma;}}}---\left(1\right)$

the method comprises the following steps that sigma is a width parameter of a function, namely a characteristic scale, and is used for controlling the radial action range of the function, the larger sigma represents the wider radial direction of the function, the larger similar code element is, the smaller sigma represents the narrower radial direction of the function, the smaller similar code element is, x and y represent two-dimensional space positions, and g (x, y and sigma) represents a two-dimensional Gaussian function;

laplace transform of formula (1):

${\mathrm{\&Delta;}}^{2}g=\frac{{\&part;}^{2}g}{\&part;x^2}+\frac{{\&part;}^{2}g}{\&part;y^2}---\left(2\right)$

wherein, Delta²g represents the Laplace function, Delta, of a two-dimensional Gaussian function²Representing a second order differential operator, g representing a two-dimensional Gaussian function;

normalized laplacian of gaussian transform:

$\begin{array}{c}{\mathrm{\&Delta;}}_{norm}^{2}g={\mathrm{\&sigma;}}^2(\frac{{\&part;}^{2}g}{\&part;x^2}+\frac{{\&part;}^{2}g}{\&part;y^2})={\mathrm{\&sigma;}}^2{\mathrm{\&Delta;}}^{2}g\\ =-\frac{1}{{\mathrm{\&pi;\&sigma;}}^2}\&lsqb;1-\frac{x^2+y^2}{2{\mathrm{\&sigma;}}^2}\&rsqb;\&CenterDot;e^{-\frac{(x^2+y^2)}{2\mathrm{\&sigma;}}}\end{array}---\left(3\right)$

in the formula (3)Represents a normalized laplacian of gaussian function with a variance of 0;

the normalized two-dimensional laplacian function shown in the formula (3) is a circularly symmetric function, and two-dimensional code elements of different sizes are detected by changing the value of σ to obtainObtaining the pole value is equivalent to solving the following equation:

$\frac{\&part;\left({\mathrm{\&Delta;}}_{norm}^{2}g\right)}{\&part;\mathrm{\&sigma;}}=0$

wherein,representing a normalized Gaussian Laplace functionThe partial derivative of the sigma is calculated,representing normalized laplace function of gaussiansA pole value of (d);

that is:

$(x^2+y^2-2{\mathrm{\&sigma;}}^2)\&CenterDot;e^{-\frac{(x^2+y^2)}{2\mathrm{\&sigma;}}}=0$

r²-2σ²＝0

wherein r represents the radius of the circular code element after the binaryzation of the two-dimensional code image in the scaleThe laplacian of gaussian response value is maximized, and similarly, if a circular symbol in the image is black and white inverted, then the laplacian of gaussian response value for that symbol is scaled toThe time reaches the minimum, the scale sigma value when the Gaussian response reaches the peak value is the characteristic scale of code element detection, the discrete Laplacian response value of the binarized image under different scales is calculated, then each point in the position space is checked, if the Laplacian response value of the point is larger than or smaller than the values of other cubic space neighborhoods, the point is the detected two-dimensional code data element point, and the searching for the position space pixel pointAnd scale spaceCan be represented by the following function:

$(\hat{x},\hat{y},\hat{t})=\arg \max \min {\mathrm{local}}_{(x,y;t)}\left({\mathrm{\&Delta;}}_{norm}^{2}L\right(x,y,t\left)\right)$

the function represents the value of the point where the laplace response reaches the maximum or minimum value at the same time on the spatial position and scale, and the point is the code element to be detected; wherein t represents a scale value, (x, y) represents a spatial position, and max min local_(x，y，t)(-) denotes the maximum or minimum of the response function at spatial and scale positions, arg (-) denotes the value of the variable corresponding to the function value,represents a standard two-dimensional laplace function in scale space;

step six: dynamically changing the size of the average template according to the diameter size of the lattice code element obtained in the step five, further carrying out dynamic mean value filtering processing on the gray level image obtained in the step two, and then carrying out binaryzation processing combining a kittler algorithm and an improved Bernsen algorithm on the gray level image subjected to dynamic mean value filtering;

the dynamic mean filtering comprises the following steps:

(1) template size was obtained by the following formula:

wherein round represents a rounding operation;

(2) calculating an average filtering template:

wherein k is the size of the average template, D is the diameter of the lattice code element, and is the average template;

(3) the average filtering is performed as follows:

I₁(x，y)＝*I(x，y)

wherein I (x, y) represents a gray-scale map matrix, I₁(x, y) represents a filtered image gray matrix;

the binaryzation processing combining the kittler algorithm and the improved Bernsen algorithm comprises the following specific steps:

(1) obtaining a global threshold T by using a Kittler algorithm, wherein the method for calculating the global threshold T comprises the following steps:

$T=\frac{\underset{x}{\mathrm{\&Sigma;}}\underset{y}{\mathrm{\&Sigma;}}e(x,y)f(x,y)}{\underset{x}{\mathrm{\&Sigma;}}\underset{y}{\mathrm{\&Sigma;}}e(x,y)}$

wherein f (x, y) is the original gray scale map obtained in step two, and e (x, y) is max { | e_x|，|e_yI.e. the maximum value of the gradient, e_xF (x-1, y) -f (x +1, y) is the gradient in the horizontal direction, e_yF (x, y-1) -f (x, y +1) is the gradient in the vertical direction;

(2) processing the interval with uneven illumination in the image histogram by adopting a Bernsen algorithm, wherein the gray value of the interval is concentrated near the global threshold T obtained in the step (1),

if T is₃D is the interval width processed by Bernsen algorithm, namely the diameter of lattice element

The result of binarization

If T is₃D is the interval width processed by Bernsen algorithm, namely the diameter of lattice element

The result of binarization

Wherein,

T₃is the basis for threshold selection and T₃(x，y)＝max_s-min_s，

T₂(x，y)＝0.5(max_d+min_d)，

T₄(x，y)＝0.5(T+T₂(x，y))；

Wherein,

max_drepresenting the maximum pixel value of the pixel point (x, y) in a window with the size of 4w by 4 w;

representing the minimum pixel value of a pixel (x, y) in a large window of 4w by 4 w;

representing the maximum pixel value of a pixel (x, y) in a smaller window of size 2w x 2 w;

representing a pixelPoint (x, y) minimum pixel value in a smaller window of size 2w x 2 w;

in the formula, max represents the maximum value of the pixel in the window, min represents the minimum value of the pixel in the window, k and l are integers and respectively represent the increment in the directions of coordinates x and y; w represents a window of local threshold operation, the value range of w is 5-9, and T is₂Representing the mean value of the pixel grey levels, T, within the window₄Is T₂Average with a global threshold T;

step seven: and carrying out morphological opening operation and closing operation on the binary image obtained in the step six to obtain a processed dot matrix image, wherein,

the opening operation is represented by the following equation:

the closing operation is represented by the following equation:

$A\&CenterDot;B=(A\&CirclePlus;B)\mathrm{\&Theta;}B$

wherein A is an input binary image, B is a square structural element, the side length of the square structural element B in the open operation is 1/3 of the lattice code element diameter, and the side length of the square structural element B in the closed operation is 1 to 5 display pixel points smaller than the lattice code element diameter;

step eight: converting the dot matrix image obtained in the seventh step into a standard DM two-dimensional code image of a recognizable block structure through median filtering denoising, wherein the median filtering denoising is realized through the following steps:

replacing the value of a point in the image with the median of the values of the points in a neighborhood of the point, and making the surrounding pixel values close to the true values, thereby eliminating isolated noise points, wherein the two-dimensional median filter output is obtained by the following formula:

g(x，y)＝med{f(x-k，y-l)，(k，l∈W)}

wherein med { } represents the median of the access array sequence; f (x, y), g (x, y) are respectively an original image and a processed image; w is a two-dimensional template of 3 x 3 size; k and l are integers and respectively represent increment in the x and y directions of the coordinates.

2. A dot-matrix DM two-dimensional code image processing device comprises:

the image reading module is used for reading the dot matrix DM two-dimensional code image and performing width unification processing by utilizing a nearest neighbor interpolation algorithm or a bilinear interpolation algorithm on the basis of not changing the high width proportion of the original dot matrix DM two-dimensional code image;

the gray level conversion module is used for converting the images with uniform sizes into gray level images;

the Gaussian smoothing filtering module is used for performing Gaussian smoothing filtering processing on the grayed image to remove fine textures of an image background and enable solid points of the dot matrix code elements to be smoother;

the binarization conversion module is used for converting the gray level image after Gaussian smooth filtering into a black and white binarization image, and specifically comprises the following steps:

(1) obtaining a global threshold T by using a Kittler algorithm, wherein the method for calculating the global threshold T comprises the following steps:

$T=\frac{\underset{x}{\mathrm{\&Sigma;}}\underset{y}{\mathrm{\&Sigma;}}e(x,y)f(x,y)}{\underset{x}{\mathrm{\&Sigma;}}\underset{y}{\mathrm{\&Sigma;}}e(x,y)}$

wherein f (x, y) is the original gray scale map obtained in step two, and e (x, y) is max { | e_x|，|e_yI.e. the maximum value of the gradient, e_xF (x-1, y) -f (x +1, y) is the gradient in the horizontal direction, e_yF (x, y-1) -f (x, y +1) is the gradient in the vertical direction;

(2) scanning the whole f (x, y) gray level image, the binarization result is:

$b(x,y)=\left\{\begin{array}{cc}255& f(x,y)>T\\ 0& f(x,y)<T\end{array}\right.$

wherein b (x, y) is a binarization result;

the code element detection module is used for carrying out code element detection on the image subjected to binarization in the binarization conversion module to obtain the diameter of the dot matrix code element, and the specific flow of the code element detection is as follows:

detecting the image elements by using a Gaussian Laplacian operator, and for a two-dimensional Gaussian function:

$g(x,y,\mathrm{\&sigma;})=\frac{1}{2\mathrm{\&pi;}\mathrm{\&sigma;}}{e}^{-\frac{(x^2+y^2)}{2\mathrm{\&sigma;}}}---\left(1\right)$

the method comprises the following steps that sigma is a width parameter of a function, namely a characteristic scale, and is used for controlling the radial action range of the function, the larger sigma represents the wider radial direction of the function, the larger similar code element is, the smaller sigma represents the narrower radial direction of the function, the smaller similar code element is, x and y represent two-dimensional space positions, and g (x, y and sigma) represents a two-dimensional Gaussian function;

laplace transform of formula (1):

${\mathrm{\&Delta;}}^{2}g=\frac{{\&part;}^{2}g}{\&part;x^2}+\frac{{\&part;}^{2}g}{\&part;y^2}---\left(2\right)$

wherein, Delta²g represents the Laplace function, Delta, of a two-dimensional Gaussian function²Representing a second order differential operator, g representing a two-dimensional Gaussian function;

normalized laplacian of gaussian transform:

$\begin{array}{c}{\mathrm{\&Delta;}}_{norm}^{2}g={\mathrm{\&sigma;}}^2(\frac{{\&part;}^{2}g}{\&part;x^2}+\frac{{\&part;}^{2}g}{\&part;y^2})={\mathrm{\&sigma;}}^2{\mathrm{\&Delta;}}^{2}g\\ =-\frac{1}{{\mathrm{\&pi;\&sigma;}}^2}\&lsqb;1-\frac{x^2+y^2}{2{\mathrm{\&sigma;}}^2}\&rsqb;\&CenterDot;e^{-\frac{(x^2+y^2)}{2\mathrm{\&sigma;}}}\end{array}---\left(3\right)$

in the formula (3)Represents a normalized laplacian of gaussian function with a variance of 0;

the normalized two-dimensional laplacian function shown in the formula (3) is a circularly symmetric function, and two-dimensional code elements of different sizes are detected by changing the value of σ to obtainObtaining the pole value is equivalent to solving the following equation:

$\frac{\&part;\left({\mathrm{\&Delta;}}_{norm}^{2}g\right)}{\&part;\mathrm{\&sigma;}}=0$

wherein,representing a normalized Gaussian Laplace functionThe partial derivative of the sigma is calculated,representation normalized Gaussian LaplaceFunction(s)A pole value of (d);

that is:

$(x^2+y^2-2{\mathrm{\&sigma;}}^2)\&CenterDot;e^{-\frac{(x^2+y^2)}{2\mathrm{\&sigma;}}}=0$

r²-2σ²＝0

wherein r represents the radius of the circular code element after the binaryzation of the two-dimensional code image in the scaleThe laplacian of gaussian response value is maximized, and similarly, if a circular symbol in the image is black and white inverted, then the laplacian of gaussian response value for that symbol is scaled toThe time reaches the minimum, the scale sigma value when the Gaussian Laplace response reaches the peak value is the characteristic scale of code element detection, the discrete Laplace response value of the binarized image under different scales is calculated, then each point in the position space is checked, if the Laplace response value of the point is larger or smaller than the adjacent domain of other cubic spaces, the discrete Laplace response value is obtainedThen that point is the detected two-dimensional code data element point, the search location spaceAnd scale spaceCan be represented by the following function:

$(\hat{x},\hat{y},\hat{t})=\arg \max \min {\mathrm{local}}_{(x,y;t)}\left({\mathrm{\&Delta;}}_{norm}^{2}L\right(x,y,t\left)\right)$

the function represents the value of the point where the laplace response reaches the maximum or minimum value at the same time on the spatial position and scale, and the point is the code element to be detected; wherein t represents a scale value, (x, y) represents a spatial position, and max min local_(x，y，t)(-) denotes the maximum or minimum of the response function at spatial and scale positions, arg (-) denotes the value of the variable corresponding to the function value,indicating the standard two-dimensional Laplace in scale spaceA function;

the dynamic mean value filtering and binary conversion module is used for dynamically changing the size of the mean template according to the diameter size of the lattice code element obtained by the code element detection module, further carrying out dynamic mean value filtering processing on the gray level image obtained by the gray level conversion module, and then carrying out binary processing combining a kittler algorithm and an improved Bernsen algorithm on the gray level image after the dynamic mean value filtering; the dynamic mean filtering comprises the following steps:

(1) template size was obtained by the following formula:

wherein round represents a rounding operation;

(2) calculating an average filtering template:

wherein k is the size of the average template, D is the diameter of the lattice code element, and is the average template;

(3) the average filtering is performed as follows:

I₁(x，y)＝*I(x，y)

wherein I (x, y) represents a gray-scale map matrix, I₁(x, y) represents a filtered image gray matrix;

the binaryzation processing combining the kittler algorithm and the improved Bernsen algorithm comprises the following specific steps:

(1) obtaining a global threshold T by using a Kittler algorithm, wherein the method for calculating the global threshold T comprises the following steps:

$T=\frac{\underset{x}{\mathrm{\&Sigma;}}\underset{y}{\mathrm{\&Sigma;}}e(x,y)f(x,y)}{\underset{x}{\mathrm{\&Sigma;}}\underset{y}{\mathrm{\&Sigma;}}e(x,y)}$

wherein f (x, y) is the original gray scale map obtained in step two, and e (x, y) is max { | e_x|，|e_yI.e. the maximum value of the gradient, e_xF (x-1, y) -f (x +1, y) is the gradient in the horizontal direction, e_yF (x, y-1) -f (x, y +1) is the gradient in the vertical direction;

(2) processing the interval with uneven illumination in the image histogram by adopting a Bernsen algorithm, wherein the gray value of the interval is concentrated near the global threshold T obtained in the step (1),

if T is₃D is the interval width processed by Bernsen algorithm, namely the diameter of lattice element

The result of binarization

If T is₃D is the interval width processed by Bernsen algorithm, namely the diameter of lattice element

The result of binarization

Wherein,

T₃is the basis for threshold selection and T₃(x，y)＝max_s-min_s，

T₂(x，y)＝0.5(max_d+min_d)，

T₄(x，y)＝0.5(T+T₂(x，y))；

Wherein,

max_drepresenting the maximum pixel value of the pixel point (x, y) in a window with the size of 4w by 4 w;

representing the minimum pixel value of a pixel (x, y) in a large window of 4w by 4 w;

representing the maximum pixel value of a pixel (x, y) in a smaller window of size 2w x 2 w;

representing the minimum pixel value of a pixel (x, y) in a smaller window of size 2w x 2 w;

in the formula, max represents the maximum value of the pixel in the window, min represents the minimum value of the pixel in the window, k and l are integers and respectively represent the increment in the directions of coordinates x and y; w represents a window of local threshold operation, the value range of w is 5-9, and T is₂Representing the mean value of the pixel grey levels, T, within the window₄Is T₂Average with a global threshold T;

an operation module for performing morphological open operation and close operation on the binary image obtained by the dynamic mean filtering and binary conversion module to obtain a processed dot matrix image,

the opening operation is represented by the following equation:

the closing operation is represented by the following equation:

$A\&CenterDot;B=(A\&CirclePlus;B)\mathrm{\&Theta;}B$

wherein A is an input binary image; b is a square structural element, the side length of the square structural element B in the open operation is 1/3 of the lattice code element diameter, and the side length of the square structural element B in the closed operation is 1 to 5 display pixel points smaller than the lattice code element diameter;

the standardization module is used for converting the dot matrix image obtained by the operation module into a standard DM two-dimensional code image of a recognizable block structure through median filtering denoising processing, and the median filtering denoising processing is realized through the following steps:

replacing the value of a point in the image with the median of the values of the points in a neighborhood of the point, and making the surrounding pixel values close to the true values, thereby eliminating isolated noise points, wherein the two-dimensional median filter output is obtained by the following formula:

g(x，y)＝med{f(x-k，y-l)，(k，l∈W)}

wherein med { } represents the median of the access array sequence; f (x, y), g (x, y) are respectively an original image and a processed image; w is a two-dimensional template of 3 x 3 size; k and l are integers and respectively represent increment in the x and y directions of the coordinates.