CN109829861B - Cold reflection suppression method based on wavelet decomposition - Google Patents
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Abstract
The invention relates to a cold reflection suppression method based on wavelet decomposition, which comprises the following steps: performing wavelet transformation on the infrared image by utilizing a wavelet basis to obtain an approximate decomposition coefficient and a high-frequency decomposition coefficient; fitting the approximate decomposition coefficient by using a least square method to obtain a minimum value point, and obtaining a minimum value by using the minimum value point; performing linear adjustment on the approximate decomposition coefficient by taking the minimum value as a reference to obtain a first approximate decomposition coefficient; carrying out nonlinear constraint on the first approximate decomposition coefficient to obtain a second approximate decomposition coefficient; and performing wavelet reconstruction by using the second approximate decomposition coefficient and the high-frequency decomposition coefficient to obtain an infrared image after the cold reflection is suppressed. The embodiment of the invention performs cold reflection inhibition processing on the approximate decomposition coefficient difference of the infrared image through wavelet decomposition, has the advantage of high universality, does not need to modify an imaging system, can complete cold reflection inhibition by processing the acquired image information, and has the advantage of low cost.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a cold reflection suppression method based on wavelet decomposition.
Background
The thermal imaging system consists of an infrared objective lens, a scanning mechanism, a detector, an electronic processing unit, a display and the like. In order to make the detector work normally, the detector must be placed in a low-temperature cavity at 195 ℃ below zero for cooling, and due to obvious temperature difference between the detector and other parts of a lens barrel, the detector receives normal imaging scenery radiation and receives images of the detector and the cold environment of the surrounding low-temperature cavity through weak reflection of a refraction surface in an infrared optical system, namely, a cold reflection effect. It generally appears on the image that there are uneven bright corners all around. The effect is influenced by the external temperature, the effect is ubiquitous in the non-refrigeration type infrared camera, and an accurate rule changing along with the temperature is difficult to find. The cold reflection causes unevenness of an image, so that the image effect is degraded. The strong cold reflection signal will overwhelm the target signal, seriously affecting the detection, identification, resolution and tracking performance of the system. It is therefore essential to suppress the cold reflection effect in advance before processing the infrared image or video.
The patent CN104297935A proposes a refrigeration type infrared imaging system and a cold reflection eliminating method thereof, wherein cold light source radiation of a detector forms circularly polarized light after passing through a polarizer and a λ/4 wave plate, the circularly polarized light is reflected back to a light path through a lens or other elements, and passes through the λ/4 wave plate again to form linearly polarized light with a polarization direction perpendicular to a transmission axis of the polarizer, the phase delay is pi/2, the linearly polarized light reflected back to the light path is blocked by the polarizer and is not transmitted again and converged to an image plane of the detector for imaging, thereby eliminating cold reflection of the refrigeration type infrared imaging.
The above patents are directed at improvement of an infrared optical system, namely, the radiation of a cold reflection cold light source is weakened in a detector, and the infrared detector has the defects of weak universality and higher cost.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a cold reflection suppression method based on wavelet decomposition. The technical problem to be solved by the invention is realized by the following technical scheme:
the invention provides a cold reflection suppression method based on wavelet decomposition, which comprises the following steps:
performing wavelet transformation on the infrared image by utilizing a wavelet basis to obtain an approximate decomposition coefficient and a high-frequency decomposition coefficient;
fitting the approximate decomposition coefficient by using a least square method to obtain a minimum value point, and obtaining a minimum value by using the minimum value point;
performing linear adjustment on the approximate decomposition coefficient by taking the minimum value as a reference to obtain a first approximate decomposition coefficient;
carrying out nonlinear constraint on the first approximate decomposition coefficient to obtain a second approximate decomposition coefficient;
and performing wavelet reconstruction by using the second approximate decomposition coefficient and the high-frequency decomposition coefficient to obtain an infrared image after the cold reflection is suppressed.
In one embodiment of the present invention, wavelet transforming an infrared image using wavelet bases to obtain approximate decomposition coefficients and high frequency decomposition coefficients, comprises:
and performing wavelet decomposition on the infrared image by using a bior wavelet basis to obtain the approximate decomposition coefficient and the high-frequency decomposition coefficient.
In an embodiment of the present invention, fitting the approximate decomposition coefficient by using a least square method to obtain a minimum value point, and obtaining a minimum value by using the minimum value point includes:
carrying out mean value filtering noise reduction processing on the approximate decomposition coefficient;
performing quadratic function fitting on the approximate decomposition coefficient subjected to noise reduction processing to obtain a minimum value point;
and obtaining a minimum value by using the minimum value point and the approximate decomposition coefficient.
In one embodiment of the present invention, performing linear adjustment on the approximate decomposition coefficient with reference to the minimum value to obtain a first approximate decomposition coefficient includes:
and resetting the approximate decomposition coefficient through a linear adjustment comparison formula taking the minimum value as a reference in the process of traversing the approximate decomposition coefficient to obtain the first approximate decomposition coefficient.
In one embodiment of the present invention, the comparison formula is:
wherein p is a linear constraint parameter;
c (i) is the ith of the approximated decomposition coefficients;
c1(i) is the i-th approximate decomposition coefficient of the first approximate decomposition coefficient;
x _ min is the minimum value point of the approximate decomposition coefficient;
and C (x _ min) is a minimum value obtained by fitting the approximate decomposition coefficient to the minimum value point.
In one embodiment of the present invention, non-linearly constraining the first approximate decomposition coefficient to obtain a second approximate decomposition coefficient includes:
and in the process of traversing the first approximate decomposition coefficient, carrying out nonlinear constraint on each number in the first approximate decomposition coefficient by using a nonlinear constraint formula.
In one embodiment of the present invention, the nonlinear constraint equation comprises:
wherein T (1) is a substitution parameter;
c1(1) is the 1 st number of the first approximate decomposition coefficients;
x _ min is the minimum value point of the approximate decomposition coefficient;
c1(x _ min) is a minimum value obtained by fitting the first approximate decomposition coefficient to a minimum value point;
i is the ith number of the approximate decomposition coefficient;
c1(i) is the i-th approximate decomposition coefficient of the first approximate decomposition coefficient;
c2(i) is the ith of the second approximate decomposition coefficients.
Compared with the prior art, the invention has the beneficial effects that:
the method performs cold reflection inhibition processing on the approximate decomposition coefficient difference of the infrared image low frequency domain through wavelet decomposition, has the advantage of high universality, does not need to modify an imaging system, can complete cold reflection inhibition only by processing the acquired image information, and has the advantage of low cost.
Other aspects and features of the present invention will become apparent from the following detailed description, which proceeds with reference to the accompanying drawings. It is to be understood, however, that the drawings are designed solely for purposes of illustration and not as a definition of the limits of the invention, for which reference should be made to the appended claims. It should be further understood that the drawings are not necessarily drawn to scale and that, unless otherwise indicated, they are merely intended to conceptually illustrate the structures and procedures described herein.
Drawings
Fig. 1 is a schematic flow chart of a cold reflection suppression method based on wavelet decomposition according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a comparison between an approximate decomposition coefficient and a linearly adjusted approximate decomposition coefficient according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a comparison between an approximate decomposition coefficient and an approximate decomposition coefficient after a nonlinear constraint according to an embodiment of the present invention;
fig. 4a to 4b are an original image and an image obtained by subjecting the original image to a cold reflection suppressing method based on wavelet decomposition.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto. For a typical infrared camera, the cold reflection can be regarded as the fact that the CCD (Charge coupled Device) of the camera receives infrared radiation emitted by itself and other structures inside the camera, resulting in uneven brightness angles around the infrared image.
Referring to fig. 1, fig. 1 is a schematic flow chart of a cold reflection suppression method based on wavelet decomposition according to an embodiment of the present invention. The embodiment of the invention provides a cold reflection suppression method based on wavelet decomposition, which comprises the following steps:
performing wavelet transformation on the infrared image by utilizing a wavelet basis to obtain an approximate decomposition coefficient and a high-frequency decomposition coefficient;
fitting the approximate decomposition coefficient by using a least square method to obtain a minimum value point, and obtaining a minimum value by using the minimum value point;
performing linear adjustment on the approximate decomposition coefficient by taking the minimum value as a reference to obtain a first approximate decomposition coefficient;
carrying out nonlinear constraint on the first approximate decomposition coefficient to obtain a second approximate decomposition coefficient;
and performing wavelet reconstruction by using the second approximate decomposition coefficient and the high-frequency decomposition coefficient to obtain an infrared image after the cold reflection is suppressed.
Specifically, the invention is realized by the following steps:
step 1, performing wavelet transformation on the infrared image by utilizing wavelet basis to obtain approximate decomposition coefficients and high-frequency decomposition coefficients.
The method for obtaining approximate decomposition coefficients and high-frequency decomposition coefficients by performing wavelet transformation on the infrared image by using wavelet basis comprises the following steps: and performing wavelet decomposition on the infrared image by using a bior (bior orthogonal wavelet) wavelet basis to obtain the approximate decomposition coefficient and the high-frequency decomposition coefficient.
The bior wavelet has better smoothness, and can well avoid blocking effect generated during reconstruction, so the embodiment of the invention performs wavelet decomposition on the input infrared image by using the bior wavelet.
Further, the wavelet decomposition coefficients after the wavelet transform are composed of approximate decomposition coefficients and detail decomposition coefficients.
In the embodiment of the invention, the difference of the approximate decomposition coefficients is mainly inhibited.
In the embodiment of the invention, approximate decomposition coefficients and high-frequency decomposition coefficients are generated after bior wavelet base decomposition, wherein the approximate decomposition coefficients form low-frequency sub-images, the high-frequency decomposition coefficients form high-frequency sub-images, the high-frequency sub-images are used for compensating image information, and the low-frequency sub-images are used for inhibiting cold reflection.
The wavelet decomposition level number is set to J, wherein the wavelet decomposition level number J is related to the smoothness of the selected wavelet function. For example: the smaller the wavelet decomposition layer number J is, the poorer the smoothness of the wavelet function is, but the smaller the operation amount of the wavelet function is; the larger the wavelet decomposition layer number J is, the better the smoothness of the wavelet function is, but the larger the computation amount of the wavelet function is, and the larger the decomposition layer number is, the larger the information amount lost by the top layer is, so that the layer number based on wavelet decomposition is not suitable to be too high.
Experiments prove that the wavelet decomposition layer number J-3 or J-5 has almost no difference from the reconstructed image quality, and the requirement can be met by selecting the wavelet decomposition layer number J-3.
Therefore, the embodiment of the invention performs 3-layer wavelet decomposition on the infrared image by using the bior wavelet basis to obtain the approximate decomposition coefficient.
And 2, fitting the approximate decomposition coefficient by using a least square method to obtain an extremely small value point, and obtaining an extremely small value by using the extremely small value point.
Fitting the approximate decomposition coefficient by using a least square method to obtain a minimum value point, and obtaining a minimum value by using the minimum value point, wherein the method comprises the following steps: carrying out mean value filtering noise reduction processing on the approximate decomposition coefficient; performing quadratic function fitting on the approximate decomposition coefficient subjected to noise reduction processing to obtain a minimum value point; and obtaining a minimum value by using the minimum value point and the approximate decomposition coefficient.
Through experimental data testing, the approximate decomposition coefficient of the infrared image with the cold reflection effect can be generally fitted into a quadratic function with an upward opening.
Firstly, mean filtering is carried out on the obtained approximate decomposition coefficient to weaken noise influence, then the quadratic function fitting is carried out on the filtered approximate decomposition coefficient, an extremely small value point is obtained, and a value in the approximate decomposition coefficient corresponding to the extremely small value point is used as an extremely small value.
Specifically, the obtained approximate decomposition coefficient is set to C, and the following formula is listed:
f(x)＝ax2+bx+c (1)
Min＝C(x_min) (3)
wherein a is the quadratic term coefficient of the quadratic function, b is the first order term coefficient of the quadratic function, and c is a constant term.
(1) Representing the quadratic function f (x);
(2) representing the approximate decomposition coefficient minimum point;
(3) representing the approximate decomposition coefficient minima.
Preferably, the mean filtering is also called linear filtering, and the main method is a neighborhood averaging method, that is, the mean value is used to replace each pixel point of the original image.
And 3, performing linear adjustment on the approximate decomposition coefficient by taking the minimum value as a reference to obtain a first approximate decomposition coefficient.
Specifically, the performing linear adjustment on the approximate decomposition coefficient with the minimum value as a reference to obtain a first approximate decomposition coefficient includes: and resetting the approximate decomposition coefficient through a linear adjustment comparison formula taking the minimum value as a reference in the process of traversing the approximate decomposition coefficient to obtain the first approximate decomposition coefficient.
Setting a linear constraint parameter as p; the approximate decomposition coefficient after the linear constraint is set to C1. Comparing the approximate decomposition coefficients with the minimum value Min obtained in the step 2 in the process of traversing the approximate decomposition coefficients (namely, sequentially performing access once and only once on each approximate decomposition coefficient in the approximate decomposition coefficients along a certain line), and resetting the coefficients according to the formula (4), namely, whether a certain approximate decomposition coefficient in the approximate decomposition coefficients is greater than the minimum value Min or not, the value of the certain approximate decomposition coefficient is linearly close to the minimum value Min, wherein the farther a certain approximate decomposition coefficient is away from the minimum value point, the stronger the linear constraint is so as to achieve the purpose of linear constraint.
Tests prove that p is 0.5, which is suitable. Referring to fig. 2, fig. 2 is a schematic diagram illustrating a comparison between an approximate decomposition coefficient and a linearly adjusted approximate decomposition coefficient according to an embodiment of the present invention.
Wherein p is a linear constraint parameter;
c (i) is the ith of the approximated decomposition coefficients;
c1(i) is the i-th approximate decomposition coefficient of the first approximate decomposition coefficient;
x _ min is the minimum value point of the approximate decomposition coefficient;
and C (x _ min) is a minimum value obtained by fitting the approximate decomposition coefficient to the minimum value point.
Preferably, the traversal modes are 3, namely a forward-order traversal, a middle-order traversal and a backward-order traversal, and the traversal mode adopted in the step is the forward-order traversal.
And 4, carrying out nonlinear constraint on the first approximate decomposition coefficient to obtain a second approximate decomposition coefficient.
Specifically, the non-linearly constraining the first approximate decomposition coefficient to obtain a second approximate decomposition coefficient includes: and in the process of traversing the first approximate decomposition coefficient, carrying out nonlinear constraint on each number in the first approximate decomposition coefficient by using a nonlinear constraint formula.
The approximate decomposition coefficient after the nonlinear constraint is C2. And in the process of traversing the linearly adjusted first approximate decomposition coefficient C1, carrying out nonlinear constraint on each number in the first approximate decomposition coefficient according to a formula (5) and a formula (6), namely the farther a certain approximate decomposition coefficient is from a minimum value point, the stronger the nonlinear constraint is, so as to achieve the purpose of nonlinear constraint.
Referring to fig. 3, fig. 3 is a schematic diagram illustrating a comparison between an approximate decomposition coefficient and an approximate decomposition coefficient after a nonlinear constraint according to an embodiment of the present invention.
Wherein T (1) is a substitution parameter;
c1(1) is the 1 st number of the first approximate decomposition coefficients;
x _ min is the minimum value point of the approximate decomposition coefficient;
c1(x _ min) is a minimum value obtained by fitting the first approximate decomposition coefficient to a minimum value point;
i is the ith number of the approximate decomposition coefficient;
c1(i) is the i-th approximate decomposition coefficient of the first approximate decomposition coefficient;
c2(i) is the ith of the second approximate decomposition coefficients.
Preferably, the traversal mode adopted in this step is a forward traversal.
And 5, performing wavelet reconstruction by using the second approximate decomposition coefficient and the high-frequency decomposition coefficient to obtain an infrared image after the cold reflection is suppressed.
Specifically, the second approximate decomposition coefficient and the high-frequency decomposition coefficient obtained through linear adjustment and nonlinear constraint are used for wavelet reconstruction, and since a part of image information of the low-frequency sub-image is lost in the suppression process, the lost image information is compensated by the high-frequency sub-image, so that the infrared image after cold reflection is suppressed is obtained. Referring to fig. 4a to 4b, fig. 4a to 4b are an original image and an image obtained by subjecting the original image to a cold reflection suppression method based on wavelet decomposition, respectively.
The invention has the beneficial effects that:
the method performs cold reflection inhibition processing on the approximate decomposition coefficient difference of the infrared image low frequency domain through wavelet decomposition, has the advantage of high universality, does not need to modify an imaging system, can complete cold reflection inhibition only by processing the acquired image information, and has the advantage of low cost.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (6)
1. A cold reflection suppression method based on wavelet decomposition is characterized by comprising the following steps:
performing wavelet transformation on the infrared image by utilizing a wavelet basis to obtain an approximate decomposition coefficient and a high-frequency decomposition coefficient;
fitting the approximate decomposition coefficient by using a least square method to obtain a minimum value point, and obtaining a minimum value by using the minimum value point;
performing linear adjustment on the approximate decomposition coefficient by taking the minimum value as a reference to obtain a first approximate decomposition coefficient;
carrying out nonlinear constraint on the first approximate decomposition coefficient to obtain a second approximate decomposition coefficient;
and performing wavelet reconstruction by using the second approximate decomposition coefficient and the high-frequency decomposition coefficient to obtain an infrared image after the cold reflection is suppressed.
2. The method of claim 1, wherein wavelet transforming the infrared image using wavelet bases to obtain approximate decomposition coefficients and high frequency decomposition coefficients, comprises:
and performing wavelet decomposition on the infrared image by using a bior wavelet basis to obtain the approximate decomposition coefficient and the high-frequency decomposition coefficient.
3. The method of claim 1, wherein fitting the approximate decomposition coefficients using a least squares method to obtain minima points, and wherein obtaining minima using the minima points comprises:
carrying out mean value filtering noise reduction processing on the approximate decomposition coefficient;
performing quadratic function fitting on the approximate decomposition coefficient subjected to noise reduction processing to obtain a minimum value point;
and obtaining a minimum value by using the minimum value point and the approximate decomposition coefficient.
4. The method of claim 1, wherein performing a linear adjustment of the approximate decomposition coefficients with respect to the minimum value to obtain first approximate decomposition coefficients comprises:
and resetting the approximate decomposition coefficient through a linear adjustment comparison formula taking the minimum value as a reference in the process of traversing the approximate decomposition coefficient to obtain the first approximate decomposition coefficient.
5. The method of claim 1, wherein non-linearly constraining the first approximate decomposition coefficient to obtain a second approximate decomposition coefficient comprises:
and in the process of traversing the first approximate decomposition coefficient, carrying out nonlinear constraint on each number in the first approximate decomposition coefficient by using a nonlinear constraint formula.
6. The method of claim 5, wherein the nonlinear constraint equation comprises:
wherein T (1) is a substitution parameter;
c1(1) is the 1 st number of the first approximate decomposition coefficients;
x _ min is the minimum value point of the approximate decomposition coefficient;
c1(x _ min) is a minimum value obtained by fitting the first approximate decomposition coefficient to a minimum value point;
i is the ith number of the approximate decomposition coefficient;
c1(i) is the i-th approximate decomposition coefficient of the first approximate decomposition coefficient;
c2(i) is the ith of the second approximate decomposition coefficients.
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Citations (3)
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| US4450479A (en) * | 1981-04-29 | 1984-05-22 | U.S. Philips Corporation | Thermal imaging apparatus |
| CN103932677A (en) * | 2014-01-05 | 2014-07-23 | 香港应用科技研究院有限公司 | Image projector |
| CN104297935A (en) * | 2013-11-27 | 2015-01-21 | 中国航空工业集团公司洛阳电光设备研究所 | Refrigeration-type infrared imaging system and cold reflection elimination device and method thereof |
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| US8466964B2 (en) * | 2007-04-02 | 2013-06-18 | Opto-Knowledge Systems, Inc. | Multispectral uncooled thermal infrared camera system |
| CN105509771B (en) * | 2015-12-08 | 2018-12-21 | 中国航空工业集团公司北京长城航空测控技术研究所 | A kind of signal de-noising method of motor oil metallic particles on-line monitoring |
| CN109035362B (en) * | 2018-06-11 | 2022-11-18 | 西安电子科技大学 | Cold reflection eliminating method based on cold reflection intensity model |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4450479A (en) * | 1981-04-29 | 1984-05-22 | U.S. Philips Corporation | Thermal imaging apparatus |
| CN104297935A (en) * | 2013-11-27 | 2015-01-21 | 中国航空工业集团公司洛阳电光设备研究所 | Refrigeration-type infrared imaging system and cold reflection elimination device and method thereof |
| CN103932677A (en) * | 2014-01-05 | 2014-07-23 | 香港应用科技研究院有限公司 | Image projector |
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