CN112950589A - Dark channel prior defogging algorithm of multi-scale convolution neural network - Google Patents
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Abstract
The invention discloses a dark channel prior defogging algorithm of a multi-scale convolutional neural network, which is characterized in that the original fog image is subjected to minimum filtering processing by using a dark channel prior theory, and the illumination intensity mean value of the pixel point position with the brightness of 0.1 percent in the dark color image is taken as a global atmospheric light value; and then estimating the fine transmittance by using a multi-scale convolution neural network, wherein the proposed algorithm consists of a coarse-scale network for performing global transmission map estimation on the whole image and a fine-scale network for locally refining the transmittance, and finally, the global atmospheric light value and the fine transmittance are substituted into an atmospheric scattering model to recover a fog-free image.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a dark channel prior defogging algorithm of a multi-scale convolution neural network.
Background
Under the severe weather conditions of fog, thunder and the like, the suspended particles in the air can influence the outdoor shooting condition of the optical imaging equipment, and the absorption, scattering and refraction of the turbid medium on the reflected light can cause the attenuation of incident light of a camera for acquiring an image of an object, so that the imaging quality is turbid, and the extraction of valuable information in the surrounding environment by the camera is seriously influenced.
Based on the defogging method of the traditional image enhancement technology, the problem of image degradation caused by attenuation of incident light rays under the foggy weather condition is not generally considered, but the image enhancement technology is directly adopted to appropriately enhance the contrast, brightness or saturation of the foggy weather image so as to highlight valuable information in the degraded image; the defogging method based on the polarization characteristic generally needs more than two specific cameras to realize, and the core idea is to utilize a plurality of images shot from different polarization angles to perform defogging. The defogging method based on the fusion strategy characteristics extracts the characteristics of the input foggy images, then fuses the foggy images by utilizing the pyramid multi-scale technology, thereby realizing the improvement of the visual effect of the foggy images, and classifies the extracted characteristics into a single-scale fusion strategy (SSR) and a multi-scale fusion strategy (MSR) according to the difference that the extracted characteristics come from a single image or a plurality of images. The methods mentioned above all simply improve the visual subjectivity of the foggy image by some method, and do not really realize physical defogging.
Disclosure of Invention
The invention aims to provide a dark channel prior defogging algorithm of a multi-scale convolutional neural network, and aims to solve the technical problems of poor defogging effect and long defogging time of a defogging method in the prior art.
In order to achieve the above object, the dark channel prior defogging algorithm of the multi-scale convolutional neural network adopted by the invention comprises the following steps:
acquiring an original fog image, preprocessing the original fog image, and calculating a dark channel value of the original fog image;
calculating a dark channel image and recording the parameter of each pixel point of the dark channel image;
comparing the brightness parameters of the pixel points and sequencing to obtain a global atmospheric light value;
selecting a multi-scale convolution neural network, inputting the original fog diagram for training, and obtaining refined transmittance;
and recovering the fog-free image.
And in the process of calculating the dark channel value of the original fog image, performing minimum filtering operation on the original fog image, enlarging the radius of an original matrix, filling the excessive pixel points with 255 values, and then obtaining the minimum value by using double-cycle traversal.
In the process of calculating a dark channel image and recording parameters of each pixel point of the dark channel image, the original fog image is subjected to minimum filtering processing to obtain the dark channel image, and then the brightness and the coordinates of each pixel point of the dark channel image are recorded.
The specific steps of comparing the brightness parameters of the pixel points and sequencing are that the pixel points of the dark channel image are sequenced in a descending order according to the brightness, the illumination value intensity of the pixel point position with the brightness of 0.1% in the front is found, and the coordinates of the pixel values are recorded.
And the global atmospheric light value is the average value of the illumination intensity of the pixel points with the brightness of 0.1 percent in the front.
The multi-scale convolutional neural network consists of a coarse-scale convolutional neural network and a fine-scale convolutional neural network, and the original fog diagram is input into the coarse-scale convolutional neural network for training at first and then fed into the fine-scale convolutional neural network for training together with a training result.
And in the process of recovering the fog-free image, substituting the global atmospheric light value and the refined transmittance into an atmospheric scattering model to recover the fog-free image.
According to the dark channel prior defogging algorithm of the multi-scale convolution neural network, the original fog image is subjected to minimum filtering processing by using a dark channel prior theory, and the illumination intensity mean value of the pixel point position with the brightness of 0.1% in the dark color image is taken as a global atmospheric light value; and then estimating the fine transmittance by using a multi-scale convolution neural network, wherein the proposed algorithm consists of a coarse-scale network for performing global transmission map estimation on the whole image and a fine-scale network for locally refining the transmittance, and finally, the global atmospheric light value and the fine transmittance are substituted into an atmospheric scattering model to recover a fog-free image.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart diagram of the dark channel prior defogging algorithm of the multi-scale convolutional neural network of the present invention.
FIG. 2 is a flow chart of the multi-scale convolutional neural network structure of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
In the description of the present invention, it is to be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships illustrated in the drawings, and are used merely for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention. Further, in the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Referring to fig. 1, the present invention provides a dark channel prior defogging algorithm for a multi-scale convolutional neural network, which comprises the following steps:
s1: acquiring an original fog image, preprocessing the original fog image, and calculating a dark channel value of the original fog image;
s2: calculating a dark channel image and recording the parameter of each pixel point of the dark channel image;
s3: comparing the brightness parameters of the pixel points and sequencing to obtain a global atmospheric light value;
s4: selecting a multi-scale convolution neural network, inputting the original fog diagram for training, and obtaining refined transmittance;
s5: and recovering the fog-free image.
Optionally, in the process of calculating the dark channel value of the original fog map, performing minimum filtering operation on the original fog map, enlarging the radius of the original matrix, filling the excess pixel points with 255 values, and then obtaining the minimum value by using double-loop traversal.
Optionally, in the process of calculating a dark channel image and recording the parameter of each pixel point of the dark channel image, the original fog image is subjected to minimum filtering to obtain the dark channel image, and then the brightness and the coordinate of each pixel point of the dark channel image are recorded.
Further optionally, the specific step of comparing the brightness parameters of the pixel points and sorting includes sorting the pixel points of the dark channel image in a descending order according to the brightness, finding the illumination value intensity of the pixel point position with the brightness being 0.1% of the first brightness, and recording the coordinates of the pixel values.
Optionally, the global atmospheric light value is an illumination intensity average value of a pixel point with the brightness of 0.1% in the front.
According to the atmospheric scattering model, the image captured by the high-definition camera in the foggy environment is shielded by haze, and incident light for acquiring the scene image is attenuated due to absorption, scattering and refraction of reflected light by suspended particles in the air. Therefore, the atmospheric scattering model of the degraded image in the foggy weather condition is represented by the following formula (1):
I(x)＝J(x)t(x)+A(1-t(x)) (1)
wherein, I (x) is a photographed foggy image; j (x) is a clear image after defogging; a is the global atmospheric light value; t (x) is a medium transmittance, and when the mist concentration is uniform, it can be further represented as t (x) e-βd(x)(β is the atmospheric scattering coefficient, d (x) is the scene depth); j (x) t (x) is a direct attenuation term reflecting the degree of attenuation of the object in the medium; a (1-t (x)) is atmospheric scattered light with a color shift in the scene.
The hong Kong Chinese university Hokeming et al makes statistics of a large number of outdoor images to summarize a basic rule: in most clear outdoor images, most local areas contain object shadows with pixel intensity values approaching 0, and further assuming that there are one or more points in all local areas where the pixel intensity values approach 0 on a certain color channel, the process of obtaining a dark primary image for one image J through a minimum filtering operation is described by the following formula:
wherein JcEach channel representing a color image, c ∈ { R, G, B } representingΩ (x, y) represents a region centered on the pixel (x, y) as a color label of the image.
And for the global atmospheric light value A, performing minimum filtering operation on the input fog image to obtain a dark primary color image, and calculating the illumination intensity average value of the pixel point position corresponding to the front 0.1% of the brightness value in the dark primary color image to be used as the global atmospheric light value A required for recovering the fog-free image.
For the transmittance t (x), we use a multi-scale convolutional neural network to estimate the transmittance t (x), and the overall working flow is shown in the figure I. The coarse-scale convolutional neural network is used for obtaining the coarse transmittance of the atomized image, and the network mainly comprises four parts, namely a convolutional layer, a maximum pooling layer, an upsampling layer and a linear combination layer.
Wherein the convolutional layers are composed of filters convolved with the input feature map, and the response of each convolutional layer is composed of
Is given in
And
is the feature map of the l-th layer and the l + 1-th layer;
the maximum pooling layer is used for performing down-sampling by using a filter with the size of 2 x 2 after each convolution layer because the number of characteristic images after convolution operation is large and the processing time is long if the calculation is directly performed, so that the image dimensionality reduction complexity is reduced;
the upsampling layer, because the original outdoor transmittance dimension should be the same as the input fog map transmittance dimension, and the input image is transmitted to the max-pooling layer by way of convolution kernel feature mapping, the transmittance dimension will be reduced by half, so an upsampling layer needs to be added to ensure that the final output transmittance is the same as the original input transmittance dimension. The upsampling layer, following the pooling layer, has the function of sub-sampling feature size recovery while preserving the non-linearity of the network. Each corresponding upsampling layer is defined as:
the above equation copies the feature pixel value after the maximum pooling at the (x, y) position into a 2 × 2 block of the subsequent upsampling layer;
the linear combination layer, which needs to output the multi-feature channels in the upsampling layer, performs linear combination to obtain the final output result (transmittance obtained by the coarse-scale network), and the linear combination expression is as follows:
referring to fig. 2, since the coarse-scale convolutional neural network adopts a network model with a larger size, shallow feature information of an image may be unconsciously ignored due to excessive attention on feature extraction of scene information, and thus, the transmittance t (x) obtained by the coarse-scale convolutional neural network cannot be directly used for image defogging processing. A fine-scale convolutional neural network is required to be closely connected behind the coarse-scale network model, and the obtained coarse transmittance of the coarse-scale network model is refined to obtain transmittance information closer to a real scene.
And deforming the atmospheric scattering model to obtain the following formula:
and (3) substituting the obtained atmospheric light value and refined transmittance into a formula (5) to remove haze in the image and recover a clear image.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A dark channel prior defogging algorithm for a multi-scale convolutional neural network is characterized by comprising the following steps:
acquiring an original fog image, preprocessing the original fog image, and calculating a dark channel value of the original fog image;
calculating a dark channel image and recording the parameter of each pixel point of the dark channel image;
comparing the brightness parameters of the pixel points and sequencing to obtain a global atmospheric light value;
selecting a multi-scale convolution neural network, inputting the original fog diagram for training, and obtaining refined transmittance;
and recovering the fog-free image.
2. The dark channel a priori defogging algorithm for a multiscale convolutional neural network as recited in claim 1, wherein in the calculation of the dark channel values of said original fog map,
and performing minimum filtering operation on the original fog image, expanding the radius of the original matrix, filling the excessive pixel points with 255 values, and then obtaining the minimum value by using double-cycle traversal.
3. The dark channel prior defogging algorithm according to claim 2 wherein, in the process of computing a dark channel image and recording the parameters of each pixel point of said dark channel image,
and carrying out minimum filtering processing on the original fog image to obtain the dark channel image, and then recording the brightness and the coordinates of each pixel point of the dark channel image.
4. The dark channel prior defogging algorithm according to claim 3, wherein the step of comparing the brightness parameters of the pixels and sorting the pixels comprises sorting the pixels of said dark channel image in descending order according to the brightness, finding the illumination intensity at the pixel position with the brightness of the first 0.1%, and recording the coordinates of the pixel values.
5. The dark channel a priori defogging algorithm of the multi-scale convolutional neural network according to claim 4, wherein said global atmospheric light value is an average of illumination intensities of pixels with brightness of the first 0.1% in magnitude.
6. The dark channel prior defogging algorithm according to claim 5, wherein said multi-scale convolutional neural network is composed of a coarse-scale convolutional neural network and a fine-scale convolutional neural network, and said raw fog pattern is input into said coarse-scale convolutional neural network for training and then fed into said fine-scale convolutional neural network for training together with the training result.
7. The dark channel a priori defogging algorithm for a multi-scale convolutional neural network according to claim 6, wherein in the process of recovering a fog-free image, the global atmospheric light value and the refined transmittance are substituted into an atmospheric scattering model to recover a fog-free image.
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