CN116029924A - Image processing method of infrared system by single-chip diffraction - Google Patents
Image processing method of infrared system by single-chip diffraction Download PDFInfo
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Abstract
The invention discloses an image processing method of a single-chip diffraction infrared system, which is based on a local maximum gradient priori mode and is used for solving the problem of image degradation and blurring of the single-chip diffraction infrared system. The research finds that the gradient maximum value in the neighborhood range of the pixel points of the system imaging can be reduced after the system imaging process, so that the degradation of the system imaging is caused, and the degradation process of the system imaging is met. The method is characterized in that local maximum gradient is used as a priori theory to directly restore. The method comprises the steps of inputting a degraded image two-dimensional matrix, establishing an objective function for restoring a system image, and finally, iteratively solving and estimating a final clear image. In the process of analyzing the image degradation of the single-chip diffraction infrared system, an inherent priori theory is found, and the image restoration method based on the priori theory has the advantages of simplicity in implementation, good restoration effect and the like.
Description
Technical Field
The invention relates to the technical field of image processing and optical engineering, in particular to the field of image restoration and enhancement of an infrared system of single-chip diffraction, and particularly relates to an image processing method of the single-chip diffraction infrared system based on a local maximum gradient priori mode.
Background
The use of lighter diffraction lenses is a trend of future optical imaging, but the diffraction efficiency of the diffraction lenses is affected due to the processing technology, the imaging characteristics of the diffraction lenses and the like, and finally the imaging is degraded and blurred. Especially in monolithic diffraction systems, the quality of the image is more severely degraded by the lack of other correction elements. Therefore, if the real-time image deblurring can be performed from the angle of the image end, the application scene of the single-chip diffraction system can be greatly expanded.
In the infrared system of single-chip diffraction, the final imaged image is a gray scale image, so that the chromatic aberration problem is not required to be considered, the infrared imaging system is more suitable for processing at an image end, and the main problem to be solved is imaging blurring. The current mainstream solution is to use wiener filtering, which is to consider an image as a continuous signal with two-dimensional stability, independent noise and zero mean value, and aims to find out that the mean square error between the estimated clear image and the original clear image is minimum. However, the method needs to know the accurate degradation process parameters of the imaging of the optical system, and meanwhile, needs to have better estimation on the ratio of the noise power spectrum to the power spectrum of the ideal image, otherwise, the effect of image restoration is quite unsatisfactory. The nature of wiener filtering methods limits the scenarios in which they are applied.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides an infrared system image processing method of single-chip diffraction. In the analysis process of the system imaging, the gradient maximum value in the neighborhood range of the pixel points of the image is found to be reduced after the system imaging process, which leads to the degradation of the system imaging, so that the method is provided as an inherent priori for the recovery of the system image, and an infrared system image processing method aiming at single-chip diffraction is designed.
The technical scheme adopted by the method is as follows: an infrared system image processing method of single-chip diffraction, comprising the following steps:
step one: a blurred image imaged by the infrared system with single diffraction is input, and the image is a two-dimensional gray scale image matrix with the size of i x j.
Step two: an objective function of image restoration is established. The objective function is as follows:
wherein the first item
Wherein I represents the estimated sharp image, k represents the blur kernel,>
representing the convolution process, B representing the input blurred image, the term being constrained using the L2 norm to minimize the loss of the blurred image and the value of the blurred image after convolution of the sharp image with the blur kernel; the second term 2-LMG (I) | 1 Wherein the LMG represents a local maximum gradient value of the image, the term being constrained using the L1 norm for maximizing the local maximum gradient of the image; third item->
In (I)>
Representing a gradient matrix of the image, the term being constrained using an L0 norm to cull local small gradient regions of the image while preserving large gradient regions; fourth item->
The term is constrained using the L2 norm to regularize the solution of the fuzzy core; alpha, beta and lambda are weight parameters, respectively.
Step three: the blur kernel of the system imaging degeneration process is initialized. The blur kernel refers to a function that causes system degradation, a two-dimensional matrix of m×m size. The fuzzy core initialization process is as follows: according to the fuzzy core size m set by input, an m-m matrix with all 0 values is created, and then the element values with row position (m-1/2) and column position (m-1/2) are set to 0.1.
Step four: and calculating the local maximum gradient prior of the image, and performing sparse constraint as an L1 norm. The local maximum gradient refers to the maximum value of the gradient in the neighborhood range of each pixel point of the image, and the calculation mode is as follows:
wherein x represents the position of any pixel point; p (x) represents the neighborhood range of the pixel point x; y represents a pixel point in a neighborhood range P (x) of the pixel point x;
gradient values representing pixel points y; the gradient represents the sum of the change rates of the pixel points in the horizontal direction and the vertical direction, and the calculation mode is as follows:
wherein g x And g y The rates of change in the horizontal and vertical directions are shown, respectively.
Step five: the objective function in the decomposition step three becomes a sub-problem. The sub-problem of decomposing the objective function into is as follows:
formula one:
formula II:
the formula I is used for solving a clear image I; equation two is used to solve for the fuzzy kernel k.
Step six: and iteratively solving the estimated fuzzy kernel and the clear image. The solving process adopts a half-quadratic splitting method.
Compared with the prior art, the image processing method using the monolithic diffraction infrared system based on the local maximum gradient priori mode has the following advantages and innovations:
1. the method finds out the characteristic that the degradation of the image accords with the prior of the local maximum gradient in the imaging process of the single-chip diffraction infrared system.
2. The inherent local maximum gradient priori is adopted, so that the imaging recovery and enhancement can be directly carried out under the strict parameters of the optical system without measurement and estimation, and the practicability and generalization performance are realized.
3. The objective function of the proposed method is added with a tiny gradient constraint term, which can be used for eliminating tiny gradients and reserving larger gradients, so that the effect of removing imaging noise can be achieved.
Drawings
FIG. 1 is a flow chart of a method for processing an image of a single-chip diffraction infrared system according to the present invention.
Fig. 2 shows targets for infrared system imaging by single-chip diffraction.
Fig. 3 is a photographic light path diagram of a monolithically diffracted infrared system.
Fig. 4 is a captured image and an image restored by the present method, wherein fig. 4 (a) is a system captured image and fig. 4 (b) is a processed image.
Detailed Description
The invention is further described below with reference to the drawings and detailed description.
The invention provides an infrared system image processing method of single-chip diffraction. In the analysis process of system imaging, the gradient maximum value in the neighborhood range of the pixel points of the image is found to be reduced after the system imaging process, which leads to degradation of the system imaging, so the method is provided as an inherent priori for system image restoration, an infrared system image processing method aiming at single-chip diffraction is designed, and the blind restoration can be directly carried out under the condition of not needing a specific priori of an optical system.
As shown in fig. 1, the present invention provides a method for processing an image of an infrared system by single-chip diffraction, comprising the steps of:
step one: a blurred image imaged by the infrared system with single diffraction is input, and the image is a two-dimensional gray scale image matrix with the size of i x j.
Step two: an objective function of image restoration is established. The objective function is as follows:
wherein the first item
Wherein I represents the estimated sharp image, k represents the blur kernel,>
representing the convolution process, B representing the input blurred image, the term being constrained using the L2 norm to minimize the loss of the blurred image and the value of the blurred image after convolution of the sharp image with the blur kernel; the second term 2-LMG (I) | 1 Wherein the LMG represents a local maximum gradient value of the image, the term being constrained using the L1 norm for maximizing the local maximum gradient of the image; third item->
In (I)>
Representing a gradient matrix of the image, the term being constrained using an L0 norm to cull local small gradient regions of the image while preserving large gradient regions; fourth item->
The term is constrained using the L2 norm to regularize the solution of the fuzzy core; alpha, beta and lambda are weight parameters and initializations are 0.01, 0.008 and 0.004, respectively.
Step three: the blur kernel of the system imaging degeneration process is initialized. The blur kernel refers to a function that causes system degradation, a two-dimensional matrix of m×m size. The fuzzy core initialization process is as follows: according to the fuzzy core size m set by input, an m-m matrix with all 0 values is created, and then the element values with row position (m-1/2) and column position (m-1/2) are set to 0.1.
Step four: and calculating the local maximum gradient prior of the image, and performing sparse constraint as an L1 norm. The local maximum gradient refers to the maximum value of the gradient in the neighborhood range of each pixel point of the image, and the calculation mode is as follows:
wherein x represents the position of any pixel point; p (x) represents the neighborhood range of the pixel point x; y represents a pixel point in a neighborhood range P (x) of the pixel point x;
gradient values representing pixel points y; the gradient represents the sum of the change rates of the pixel points in the horizontal direction and the vertical direction, and the calculation mode is as follows:
wherein g x And g y The change rates in the horizontal and vertical directions are respectively represented, and the calculated range is 2 pixel points.
Step five: the objective function in the decomposition step three becomes a sub-problem. The sub-problem of decomposing the objective function into is as follows:
formula one:
formula II:
/>
the formula I is used for solving a clear image I; equation two is used to solve for the fuzzy kernel k.
Step six: and iteratively solving the estimated fuzzy kernel and the clear image. The solving process adopts a half-quadratic splitting method.
Step seven: outputting a final fuzzy kernel, wherein the size of the final fuzzy kernel is m; and outputting a final clear image, wherein the size of the final clear image is i x j.
The imaging image is obtained by shooting the target object by using the infrared system of single-chip diffraction, the target object adopts a checkerboard target, as shown in fig. 2, the shooting is performed by using the single-chip diffraction lens, the target object is in the imaging range, the infrared detector is on the focal plane, the shot target image is obtained by using the monitor, as shown in fig. 3, the image processing is performed by using the method of the invention, the image which is directly shot by the system shown in fig. 4 and is processed by the method is obtained, and the contrast and the definition of the image can be intuitively found to be greatly improved.
It should be understood that the foregoing description of the preferred embodiments is not intended to limit the scope of the invention, but rather to limit the scope of the claims, and that those skilled in the art can make substitutions or modifications without departing from the scope of the invention as set forth in the appended claims.
Claims (6)
1. An infrared system image processing method of single-chip diffraction is characterized in that: comprises the following steps:
step one: inputting a blurred image imaged by the infrared system of single-chip diffraction;
step two: establishing an objective function of image restoration;
step three: initializing a fuzzy kernel of a system imaging degradation process;
step four: calculating a local maximum gradient prior of the image, and performing sparse constraint as an L1 norm;
step five: decomposing the objective function in the third step into sub-problems;
step six: and iteratively solving the estimated fuzzy kernel and the clear image.
2. A method of image processing for a monolithically diffracted infrared system as claimed in claim 1, wherein: the objective function model of the image restoration is as follows:
wherein the first item
Wherein I represents the estimated sharp image, k represents the blur kernel,>
representing the convolution process, B representing the input blurred image, the term being constrained using the L2 norm to minimize the loss of the blurred image and the value of the blurred image after convolution of the sharp image with the blur kernel; the second term 2-LMG (I) | 1 Wherein the LMG represents a local maximum gradient value of the image, the term being constrained using the L1 norm for maximizing the local maximum gradient of the image; third item->
In (I)>
Representing a gradient matrix of the image, the term being constrained using an L0 norm to cull local small gradient regions of the image while preserving large gradient regions; fourth item->
The term is constrained using the L2 norm to regularize the solution of the fuzzy core; alpha, beta and lambda are weight parameters, respectively.
3. A method of processing a monolithically diffracted infrared system image according to claim 1 or 2, characterized in that: the blur kernel refers to a function that causes system degradation, which appears as a two-dimensional matrix. The fuzzy core initialization process in the second step is as follows: according to the fuzzy core size m set by input, an m-m matrix with all 0 values is created, and then the element values with row position (m-1/2) and column position (m-1/2) are set to 0.1.
4. A method of processing a monolithically diffracted infrared system image according to claim 1 or 2, characterized in that: the local maximum gradient refers to the maximum value of the gradient in the neighborhood range of each pixel point of the image, and the calculation mode is as follows:
wherein x represents the position of any pixel point; p (x) represents the neighborhood range of the pixel point x; y represents a pixel point in a neighborhood range P (x) of the pixel point x;
the gradient value of the pixel y is indicated.
5. A method of processing a monolithically diffracted infrared system image according to claim 1 or 2 or 4, wherein: the gradient represents the sum of the change rates of the pixel points in the horizontal direction and the vertical direction, and the calculation mode is as follows:
wherein g x And g y The rates of change in the horizontal and vertical directions are shown, respectively.
6. A method of processing a monolithically diffracted infrared system image according to claim 1 or 2, characterized in that: in the fifth step, the sub-problem of decomposing the objective function is as follows:
formula one:
/>
formula II:
the formula I is used for solving a clear image I; equation two is used to solve for the fuzzy kernel k.
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| CN117058038B (en) * | 2023-08-28 | 2024-04-30 | 北京航空航天大学 | Diffraction blurred image restoration method based on even convolution deep learning |
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