Image definition evaluation method for long-focus visible light imaging system

Abstract

The invention belongs to the technical field of image processing, and particularly relates to an image definition evaluation method for a long-focus visible light imaging system. According to the method, firstly, an image is preprocessed through a median filtering algorithm, the median filtering algorithm is a typical representative of a nonlinear filtering algorithm, isolated noise points can be eliminated through the median filtering algorithm, edge contour information can be effectively protected while random noise is removed, and the characteristic quite meets the height requirement of a definition function for edge information; then, NSST (non-subsampled shearlet transform) decomposition is performed on the preprocessed image to obtain a low-frequency component and a high-frequency sub-band, and a robust image definition evaluation method is constructed in combination with direction consistency and contrast normalization.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of image processing, and particularly relates to an image definition evaluation method for a long-focus visible light imaging system. BACKGROUND

[0002] Compared with satellite reconnaissance, aerial reconnaissance has the characteristics of flexibility, maneuverability, accuracy and strong pertinence, and becomes a basic means for obtaining tactical intelligence in modern war. In recent years, aerial photoelectric pod gradually develops towards small size, single load, large aperture, long focal length and multi-spectral load, and the optical system also develops from a transmission system to a Cassegrainian catadioptric system, and long-range sea reconnaissance is also a capability that a high-performance photoelectric pod must have. In order to realize long-distance detection and recognition and high target resolution, the focal length of a visible light camera must be designed to be long enough. However, the long-focus and large-aperture optical imaging system is very sensitive to environmental changes, and small changes in temperature and atmospheric pressure will cause the defocus of the optical imaging load. Therefore, the long-focus aerial optical load must have an automatic focusing function, and the automatic focusing effect in different working conditions and different scenes also affects the image effect of aerial reconnaissance. At present, the automatic focusing method used by the aerial photoelectric pod is the image method, and the working principle of automatic focusing is that the focusing motor drives the focusing group, the potentiometer or the encoder to move at the same time, and the video processing unit at the rear end of the detector calculates the image definition value of the focusing group at different positions through the image definition evaluation function. After the focusing motor completes the full stroke traversal, it is located at the clearest position determined by the image definition function, thereby completing the automatic focusing work flow. The image method depends on the image definition evaluation function, and typical definition evaluation methods mainly include spatial domain type, frequency domain type, information entropy type, statistical type and model learning type. In actual engineering application, the most commonly used method is the spatial domain method, which is based on gradient calculation and has the advantages of low calculation complexity and being suitable for embedded real-time systems, but it is severely dependent on the contrast of the image. The action distance of the long-focus visible light imaging system covers several kilometers to hundreds of kilometers, and is subject to the diffraction limit and aperture limit of the optical system, the scattering and absorption of the atmosphere, the external working environment and other factors. With the increase of reconnaissance distance, the target contrast decreases, which puts forward higher requirements for the image definition evaluation method, and a definition evaluation function with strong robustness is needed to enable the long-focus optical system to realize accurate automatic focusing at different action distances.

[0003] The existing definition evaluation methods, such as gradient function and Laplacian function, have low sensitivity in long-distance weak texture or low contrast scenes, and may cause an increase in the failure probability of automatic focusing. SUMMARY

[0004] In view of this, the present application aims to provide a long focal length visible light imaging system-oriented image sharpness evaluation method to solve the problem that the existing sharpness evaluation method has decreased sensitivity in a long distance weak texture or low contrast scene and increases the failure probability of automatic focusing. The present application can consider multi-scale features and direction information and has a weight adjustment mechanism, which can improve the evaluation accuracy and robustness of the long focal length visible light imaging system in a complex environment and effectively improve the accuracy of automatic focusing.

[0005] To achieve the above-mentioned purpose, the technical scheme of the present application is as follows: A long focal length visible light imaging system-oriented image sharpness evaluation method, specifically comprising the following steps: S1: The focusing motor of the long focal length visible light imaging system drives the focusing lens to move to obtain a focusing image; S2: The focusing image is preprocessed to obtain a preprocessed image, and the preprocessed image is sequentially subjected to global linear normalization and local contrast normalization processing to obtain a normalized image; S3: The normalized image is subjected to NSST decomposition to obtain high-frequency sub-band coefficients of different scales and different directions; S4: Based on the high-frequency sub-band coefficients of different scales and different directions, the direction consistency of different scales is calculated; S5: Based on the direction consistency of different scales, a local sharpness function of different scales and different directions is constructed; S6: The local sharpness values of different scales and different directions are weighted and fused to obtain a fused image; S7: The pixel values corresponding to each pixel point of the fused image are averaged to obtain the sharpness evaluation value of the focusing image.

[0006] Further, in step S2, the preprocessing operation is specifically a median filtering operation.

[0007] Further, in step S2, the calculation formula used for global linear normalization and local contrast normalization processing of the preprocessed image is: ; ; Wherein, is the preprocessed image, is the global linear normalized image, is the minimum value of the pixels in the preprocessed image, is the maximum value of the pixels in the preprocessed image, is the anti-zero term, is the coordinate position of the pixel point, is the local contrast normalized image, To obtain the average value of pixels within a local window W, This is to obtain the standard deviation of pixels within a local window W.

[0008] Furthermore, in step S4, the calculation formula used to calculate the directional consistency at different scales is as follows: ; in, For directional consistency at the s-scale, For the first Scale, First High-frequency subband coefficients in each direction.

[0009] Furthermore, the expressions for the local sharpness function at different scales and in different directions are as follows: ; in, For the first Scale, First Local sharpness values ​​in each direction, For directional consistency at the s-th scale, For the first Scale, First High-frequency subband coefficients in each direction.

[0010] Furthermore, the calculation formula used for weighted fusion of local sharpness values ​​at different scales and in different directions is as follows: ; ; ; in, For the first Scale, First Local sharpness values ​​in each direction, For the first Scale, First Weight coefficients for each direction, For the first Scale, First Subband energy in each direction, To merge images.

[0011] Furthermore, the formula used to average the pixel values ​​corresponding to each pixel in the fused image is as follows: ; Among them, the score is the clarity rating.

[0012] Compared with the prior art, the present invention can achieve the following beneficial effects: This invention presents an image sharpness evaluation method for long-focal-length visible light imaging systems. It employs median filtering as a preprocessing technique to remove noise interference while preserving image detail to the greatest extent possible. Contrast normalization is applied to the image to improve its adaptability to various environments. By performing NSST processing on the image, a directional consistency metric is introduced at the sub-band level and combined with sub-band energy using adaptive weights. This accurately distinguishes between true blur and energy changes caused by exposure and contrast differences. It is more sensitive to images acquired at long distances by long-focal-length optical systems (with weak texture and low contrast), effectively assisting the focusing system in achieving precise autofocus. Attached Figure Description

[0013] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic flowchart illustrating the image sharpness evaluation method for long focal length visible light imaging systems as described in an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not constitute a limitation thereof.

[0015] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0016] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0017] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0018] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] like Figure 1 As shown, this invention proposes an image sharpness evaluation method for long focal length visible light imaging systems, which specifically includes the following steps: S1: The focusing motor of the long focal length visible light imaging system drives the focusing lens to move and obtain a focused image; S2: Perform preprocessing operations on the focusing image to obtain a preprocessed image. Then, perform global linear normalization and local contrast normalization on the preprocessed image in sequence to obtain a normalized image. S3: Perform NSST decomposition on the normalized image to obtain high-frequency subband coefficients at different scales and in different directions; S4: Calculate the directional consistency at different scales based on the high-frequency subband coefficients at different scales and in different directions; S5: Based on the directional consistency at different scales, construct local sharpness functions for different scales and directions; S6: Weighted fusion of local sharpness values ​​at different scales and in different directions to obtain a fused image; S7: Take the average of the pixel values ​​corresponding to each pixel in the fused image to obtain the sharpness evaluation value of the focused image.

[0020] It should be noted that this invention first employs a median filtering algorithm to preprocess the image. This algorithm is a typical representative of nonlinear filtering algorithms. It can eliminate isolated noise points and effectively protect edge contour information while removing random noise. This characteristic is very much in line with the high requirements of the sharpness function for edge information. Then, the preprocessed image is decomposed using NSST (non-subsampled shear wave transform) to obtain low-frequency components and high-frequency subbands. Combined with directional consistency and contrast normalization, a robust image sharpness evaluation method is constructed.

[0021] In addition, when changing the focus image, S2-S7 are used to process the changed focus image.

[0022] Furthermore, NSST is a multi-scale geometric analysis tool based on shear wave theory, which has wide applications in the field of image processing. Compared with traditional transform methods (such as wavelet transform, Contourlet transform, etc.), NSST has translation invariance, multi-scale, multi-directionality, and efficient direction selectivity. Its non-subsampling characteristics and precise filtering design ensure high decomposition accuracy and can better characterize the geometric characteristics in the image. Therefore, this invention uses NSST as the underlying framework for constructing the image sharpness evaluation function.

[0023] This invention performs median filtering preprocessing on images, using NSST (Non-Subsampled Shear Wave Transform) as the basic framework, combined with multi-scale decomposition and orientation consistency. This enables long-focal-length visible light imaging systems to accurately assess image sharpness in complex scenes with low contrast and noise interference. Simultaneously, contrast normalization reduces the interference of exposure and contrast on sharpness scoring, making the scoring more robust, unifying the scoring range across different scenes, and improving cross-scene consistency and comparability.

[0024] In some embodiments, in step S2, the preprocessing operation is specifically a median filtering operation.

[0025] In some embodiments, the calculation formulas used in step S2 for performing global linear normalization and local contrast normalization on the preprocessed image are as follows: ; ; in, For preprocessing images, For globally linearly normalized images, The minimum pixel value in the preprocessed image. The maximum pixel value in the preprocessed image. To prevent zero items, The coordinates of the pixel. For locally contrast-normalized images, To obtain the average value of pixels within a local window W, This is to obtain the standard deviation of pixels within a local window W.

[0026] It should be noted that, for any pixel in the image to be processed, a local window with radius r is constructed with that pixel as the center. The mean and standard deviation of the pixel gray level are calculated within the local window, and the mean and standard deviation are used to normalize the current pixel to eliminate the influence of local brightness and contrast differences.

[0027] In some embodiments, in step S4, the calculation formula used to calculate the directional consistency at different scales is: ; in, For directional consistency at the s-scale, For the first Scale, First High-frequency subband coefficients in each direction.

[0028] In some embodiments, the expressions for the local sharpness function at different scales and in different directions are as follows: ; in, For the first Scale, First Local sharpness values ​​in each direction, For directional consistency at the s-th scale, For the first Scale, First High-frequency subband coefficients in each direction.

[0029] In some embodiments, the calculation formula used for weighted fusion of local sharpness values ​​at different scales and in different directions is as follows: ; ; ; in, For the first Scale, First Local sharpness values ​​in each direction, For the first Scale, First Weight coefficients for each direction, For the first Scale, First Subband energy in each direction, For image fusion, N represents the number of pixels in the current sub-band.

[0030] Furthermore, under different scenarios and distances, the contribution of each sub-band to the overall image sharpness can be adjusted based on the abundance of high-frequency energy, i.e., the adjustment... .

[0031] In some embodiments, the formula used to average the pixel values ​​corresponding to each pixel in the fused image is as follows: ; Among them, the score is the clarity rating.

[0032] It should be noted that after obtaining the sharpness evaluation value of the focused image, the sharpness evaluation value can be used to evaluate the image sharpness. The evaluation includes judging the sharpness of a single image and comparing the sharpness of multiple images. The higher the sharpness evaluation value, the higher the overall image sharpness.

[0033] In long-focal-length visible light imaging systems, sharpness evaluation values ​​can be used as feedback parameters to drive the focusing mechanism or control the adjustment of imaging parameters. For example, during an autofocus process, the focusing motor drives the focusing lens to move within its focusing stroke, acquiring a series of focusing images at different focusing positions. This invention evaluates the acquired focusing images to obtain sharpness evaluation values, thereby assisting the long-focal-length visible light imaging system in forming a closed-loop control and improving autofocus accuracy.

[0034] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0035] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for evaluating image sharpness in long-focal-length visible light imaging systems, characterized in that: Specifically, the steps include the following: S1: The focusing motor of the long focal length visible light imaging system drives the focusing lens to move and obtain a focused image; S2: Perform preprocessing operations on the focusing image to obtain a preprocessed image. Then, perform global linear normalization and local contrast normalization on the preprocessed image in sequence to obtain a normalized image. S3: Perform NSST decomposition on the normalized image to obtain high-frequency subband coefficients at different scales and in different directions; S4: Calculate the directional consistency at different scales based on the high-frequency subband coefficients at different scales and in different directions; S5: Based on the directional consistency at different scales, construct local sharpness functions for different scales and directions; S6: Weighted fusion of local sharpness values ​​at different scales and in different directions to obtain a fused image; S7: Take the average of the pixel values ​​corresponding to each pixel in the fused image to obtain the sharpness evaluation value of the focused image.

2. The image sharpness evaluation method for long focal length visible light imaging systems according to claim 1, characterized in that: In step S2, the preprocessing operation is specifically a median filtering operation.

3. The image sharpness evaluation method for long focal length visible light imaging systems according to claim 1, characterized in that: In step S2, the calculation formulas used for global linear normalization and local contrast normalization of the preprocessed image are as follows: ； ； in, For preprocessing images, For globally linearly normalized images, The minimum pixel value in the preprocessed image. The maximum pixel value in the preprocessed image. To prevent zero items, The coordinates of the pixel. For locally contrast-normalized images, To obtain the average value of pixels within a local window W, This is to obtain the standard deviation of pixels within a local window W.

4. The image sharpness evaluation method for long focal length visible light imaging systems according to claim 1, characterized in that: In step S4, the formula used to calculate the directional consistency at different scales is as follows: ； in, For directional consistency at the s-scale, For the first Scale, First High-frequency subband coefficients in each direction.

5. The image sharpness evaluation method for long focal length visible light imaging systems according to claim 1, characterized in that: The expressions for the local sharpness function at different scales and in different directions are: ； in, For the first Scale, First Local sharpness values ​​in each direction, For the directional consistency at the s-th scale, For the first Scale, First High-frequency subband coefficients in each direction.

6. The image sharpness evaluation method for long focal length visible light imaging systems according to claim 1, characterized in that: The calculation formula used for weighted fusion of local sharpness values ​​at different scales and in different directions is as follows: ； ； ； in, For the first Scale, First Local sharpness values ​​in each direction, For the first Scale, First Weight coefficients for each direction, For the first Scale, First Subband energy in each direction, To merge images.

7. The image sharpness evaluation method for long focal length visible light imaging systems according to claim 1, characterized in that: The formula used to average the pixel values ​​corresponding to each pixel in the fused image is as follows: ； Among them, the score is the clarity rating.

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