Optical-digital collaborative optimization zoom imaging system design method

Abstract

The invention discloses a light-digital collaborative optimization zoom imaging system design method, and belongs to the technical field of computational imaging, a four-piece zoom structure is adopted as an initial optical system, Zemax is utilized to simulate and output a blurred image and a spatial variable point spread function (PSF), and an end-to-end joint optimization platform is constructed through VS Code and Matlab; the digital processing part is used for segmenting the image and the PSF into n * n blocks based on a blocking strategy, carrying out non-blind deblurring processing by adopting a Lucy-Richardson algorithm in combination with a boundary expansion and gradient fusion technology, and optimizing the brightness through a dichotomy; and finally, feeding back the mean square error of the restored image and the original image to Zemax as an evaluation function, and driving the optimization of the optical system. According to the invention, while the complexity of the system is maintained, the restoration effect of short-focus and other scenes is remarkably improved, and the imaging quality is superior to that of a traditional independent design mode.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer imaging, and in particular relates to a design method for a zoom imaging system using optical-digital collaborative optimization. Background Art

[0002] With the rapid development of optoelectronic imaging technology, market requirements for optical system performance are increasing, including high resolution, wide field of view, and long detection distance, while also meeting design goals of lightweight, miniaturization, and low cost. Traditional zoom optical systems typically use an increased number of lenses, complex surface designs, and precise mechanical structures to correct aberrations and achieve focal length adjustment. However, this results in larger system size and increased costs, which conflicts with current application demands for portability and integration.

[0003] like Figure 1 As shown, in traditional design approaches, optical systems and image processing algorithms are often optimized independently: optical design pursues hardware-level aberration minimization, while digital algorithms are used only as a post-processing measure. This fragmented optimization approach makes it difficult to achieve a global optimum and easily leads to wasteful resources: the optical system may become overly complex due to overdesign, while the potential of the digital algorithms is underutilized. This is particularly true in zoom systems, where the coordinated motion of the zoom and compensation groups significantly impacts image quality. Traditional approaches struggle to balance aberration correction and system complexity across the entire focal range.

[0004] Optical-digital joint optimization technology offers a new approach to solving this problem. By collaboratively optimizing optical design and image processing algorithms, the respective strengths can be fully leveraged to achieve a better balance between system complexity and image quality. However, existing joint design methods still have shortcomings in their application to zoom systems, such as the lack of adaptive processing for spatially variable aberrations and the lack of an efficient end-to-end optimization framework. Therefore, a more efficient optical-digital joint design method is urgently needed to improve the overall performance of zoom systems. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a zoom imaging system design method based on optical-digital collaborative optimization. Compared with traditional design methods, it can obtain overall better imaging effects for zoom optical systems of the same complexity, and obtain overall equal or better imaging effects for systems with higher complexity, thus solving the problem of increased complexity caused by traditional designs that only obtain local optimal solutions.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A method for designing a zoom imaging system using optical-digital collaborative optimization, the method comprising:

[0008] Step 1: The optical system outputs the blurred image of the initial optical structure and the spatially variable PSF dataset through Zemax;

[0009] Step 2: Perform image block restoration on the blurred image and the PSF dataset in Matlab to obtain a restored image. The image block restoration process includes boundary extension compensation and linear gain method merging to eliminate artifacts at the splicing boundary.

[0010] Step 3: Matlab compares the image evaluation indicators of the restored image and the initial optical structure. If the comparison result does not meet the design indicators, the comparison result is fed back to the optical system, and steps 1 to 3 are performed again with the restored image as the initial optical structure, forming a closed-loop optimization until the imaging quality requirements are met.

[0011] In a second aspect, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned optical-digital collaborative optimization zoom imaging system design method.

[0012] In a third aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned optical-digital collaborative optimization zoom imaging system design method.

[0013] The beneficial effects of the present invention are:

[0014] The optical-digital joint design zoom system and method based on the Lucy-Richardson algorithm proposed in the present invention has significant technical advantages and application value. Compared with the traditional independent design mode, this method achieves an overall improvement in system performance by constructing a collaborative optimization framework for optical design and digital image processing. At the optical design level, this method breaks through the limitations of traditional over-reliance on hardware correction, and achieves better imaging quality while maintaining or reducing system complexity by rationally allocating the optimization weights of the optical system and digital algorithm. Especially in scenarios such as short focus that are difficult to handle with traditional designs, the block processing strategy and gradient fusion technology based on spatially variable PSF effectively improve the image restoration effect, so that key indicators such as the mean square error, peak signal-to-noise ratio and structural similarity of the restored image are significantly improved.

[0015] The end-to-end optimization platform built by this system has outstanding versatility and scalability. By integrating the development environments of Zemax and Matlab through VS Code, it not only achieves seamless integration of optical simulation and image processing, but also provides flexible configuration solutions for different initial structures and application scenarios. The adopted Lucy-Richardson algorithm has been targeted and optimized, combined with precise brightness adjustment and boundary processing mechanisms, which significantly improves the application effect of the algorithm in the zoom system. Experimental results show that the final restored image quality of the four-piece zoom system optimized by this method is significantly better than that of the traditional design method, especially in key areas such as the edge field of view and short focal length. This innovative design model provides a new technical path for optical-digital collaborative optimization in the field of computational imaging, effectively improving the comprehensive imaging performance of the zoom system while ensuring the compactness and economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Flowchart for image restoration after traditional optical design;

[0017] Figure 2 This is a flow chart of a design method for a zoom imaging system with optical-digital collaborative optimization according to the present invention;

[0018] Figure 3 A two-dimensional diagram of the four-element zoom system designed for traditional optics;

[0019] Figure 4 A two-dimensional diagram of the four-piece zoom system with combined optical and digital design proposed in this invention;

[0020] Figure 5 is an image used to simulate the target to be imaged in the simulation example of the present invention;

[0021] Figure 6 The restored image of the telephoto lens in the traditional optical design;

[0022] Figure 7 This is the restored image at mid-focus in traditional optical design;

[0023] Figure 8 The restored image of the short focal length in the traditional optical design;

[0024] Figure 9 The restored image of the medium-long focal length in the optical-digital joint design proposed by the present invention;

[0025] Figure 10 This is the restored image of the middle focus in the optical-digital joint design proposed in the present invention;

[0026] Figure 11 The restored image of the short focus in the light-digital joint design proposed by the present invention;

[0027] Figure 12 This is the MSE comparison curve between the present invention and the traditional optical design method;

[0028] Figure 13 The PSNR comparison curves of the present invention and the traditional optical design method are shown in FIG.

[0029] Figure 14 This is the SSIM comparison curve between the present invention and the traditional optical design method. DETAILED DESCRIPTION

[0030] The present invention will be further described below with reference to the accompanying drawings and examples.

[0031] like Figure 2 As shown, the present invention proposes a zoom imaging system design method for optical-digital collaborative optimization, which consists of an optical design system and a digital image processing system to form a closed-loop optimization architecture. The optical design system uses Zemax; the digital image processing system is based on the Matlab image processing module. The two form an end-to-end bidirectional data channel through the VS Code control API interface. The optical design system provides a blurred image and the system's point spread function for image restoration. Matlab provides objective indicators corresponding to the current system image restoration, such as mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) to guide optical system optimization. The method specifically includes:

[0032] Step 1: Optical simulation: The optical system outputs a blurred image of the initial optical structure and a spatially variable PSF data set through Zemax.

[0033] The system loads the initial optical structure in Zemax, performs optical simulation on the input calibration image, and outputs a blurred image of the current structure and the corresponding spatially variable point spread function (PSF) data set.

[0034] Step 2, image processing; performing image block restoration processing on the blurred image and PSF dataset in Matlab to obtain a restored image, wherein the image block restoration processing includes boundary extension compensation and linear gain method merging to eliminate artifacts at the splicing boundary;

[0035] A block-by-block restoration algorithm is implemented in a Matlab digital image processing system. The input blurred image is first preprocessed using polynomial distortion correction. Then, to account for the spatially varying nature of the optical system, the preprocessed blurred image and the PSF dataset are divided into n×n processing blocks. This reduces the complex spatially varying deconvolution problem to multiple spatially invariant deconvolution problems. Deblurring is then performed using a modified Lucy-Richardson algorithm for each of the three RGB channels. A binary search method is then used to find the optimal image brightness to obtain the restored image. The image blocks are processed using an extended boundary technique. First, the overlap region is expanded by b pixels beyond the original block boundary to form an overlap region, then expanded by a pixels (ab pixels) to compensate for pixel clipping at the boundary. Finally, the image is merged using a linear gain method. This method eliminates significant deviations in the restoration results on either side of the seam caused by inconsistencies in the PSF used during deconvolution of adjacent blocks. This deviation manifests as a sudden change in image intensity values ​​and ultimately creates visible artifacts at the splicing boundary.

[0036] Step 3: Fusion feedback: Matlab compares the image evaluation indicators of the restored image and the initial optical structure. If the comparison result does not meet the design indicators, the comparison result is fed back to the optical system, and steps 1 to 3 are executed again with the restored image as the initial optical structure, forming a closed-loop optimization until the imaging quality requirements are met.

[0037] After the image is restored in Matlab, it is compared with the original image to obtain objective evaluation index data: MSE, PSNR, SSIM, and feedback is given to Zemax in the form of user operation number (UDOC) through the API interface. Zemax then uses the damped least squares method or orthogonal descent method based on the current indicators to optimize the current optical system until the system meets the requirements.

[0038] like Figure 3 、 Figure 4 As shown in Table 1, a new system is obtained after the optical-digital joint design zoom system design method of the present invention. The imaging quality of the optical system is slightly worse than that of the original system, but the restored structure is better than the original system (as shown in Table 1 and Table 2). Figure 3 A three-dimensional diagram of the four-element zoom system designed for traditional optics; Figure 4 The three-dimensional structure diagram of the four-piece zoom system with optical-digital joint design proposed by the present invention, Table 1 corresponds to Figure 3 The effect parameters of the system shown in Table 2 correspond to Figure 4The performance parameters of the system shown in the table are as follows: the optical system part refers to the objective evaluation indicators of the image output by the optical system, namely mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). The restored image part in the table refers to the objective evaluation indicators of the image obtained after Matlab image processing.

[0039] Table 1

[0040]

[0041] Table 2

[0042]

[0043] See Figure 5-Figure 12 , Figure 5 is an image used to simulate the target to be imaged in the simulation example of the present invention; Figure 6 、 Figure 7 、 Figure 8 These are the restored images of long focus, medium focus, and short focus in the traditional optical design; Figure 9 、 Figure 10 、 Figure 11 They are the restored images of long focus, medium focus and short focus obtained based on the design method of zoom imaging system with optical-digital collaborative optimization of the present invention. It can be seen that in terms of the long focus restored image, its mean square error, peak signal-to-noise ratio and structural similarity are better than those of the original system; in terms of the medium focus restored image, its mean square error, peak signal-to-noise ratio and structural similarity are comparable to those of the original system; in terms of the short focus restored image, its mean square error, peak signal-to-noise ratio and structural similarity are better than those of the original system, and the image perception is significantly improved.

[0044] Figure 12 、 Figure 13 、 Figure 14 The following are the MSE, PSNR, and SSIM comparison curves for the present invention (new system) and the traditional optical design method (old system). The optical-digital hybrid zoom system slightly balances the three structures, achieving excellent image restoration quality for each structure. The MSE for the short, medium, and long focal lengths decreases by 60%, PSNR increases by 20%, and SSIM increases by 2%. The MSE for the medium focal length decreases by 15%, PSNR increases by 5%, and SSIM increases by 0.8%. The MSE for the long focal length decreases by 30%, PSNR increases by 10%, and SSIM increases by 1%. This demonstrates that the present invention achieves superior computational accuracy in all aspects.

[0045] The present invention also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned optical-digital collaborative optimization zoom imaging system design method.

[0046] The present invention also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned optical-digital collaborative optimization zoom imaging system design method.

[0047] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A zoom imaging system design method based on optical-digital collaborative optimization, characterized in that: The method comprises: Step 1: The optical system outputs the blurred image of the initial optical structure and the spatially variable PSF dataset through Zemax; Step 2: Perform image block restoration on the blurred image and the PSF dataset in Matlab to obtain a restored image. The image block restoration process includes boundary extension compensation and linear gain method merging to eliminate artifacts at the splicing boundary. Step 3: Matlab compares the image evaluation indicators of the restored image and the initial optical structure. If the comparison result does not meet the design indicators, the comparison result is fed back to the optical system, and steps 1 to 3 are performed again with the restored image as the initial optical structure, forming a closed-loop optimization until the imaging quality requirements are met.

2. The optical-digital collaborative optimization zoom imaging system design method according to claim 1, characterized in that: Zemax and Matlab form an end-to-end bidirectional data channel through the VS Code control API interface.

3. The optical-digital collaborative optimization zoom imaging system design method according to claim 1, characterized in that: The step 2 includes: Perform polynomial distortion correction preprocessing on the input blurred image; The preprocessed blurred image and PSF dataset are divided into n×n processing blocks. The improved Lucy-Richardson algorithm is used to perform deblurring processing on the RGB channels respectively, and the binary search method is used to find the optimal image brightness to obtain the restored image.

4. The optical-digital collaborative optimization zoom imaging system design method according to claim 3, characterized in that: The processing blocks are divided into n×n blocks and an extended boundary processing technology is used. First, b pixels are extended outside the original block boundary to form an overlapping area, and then extended to a pixels, a>b, to compensate for the boundary pixel shearing, and finally the linear gain method is used for merging.

5. The optical-digital collaborative optimization zoom imaging system design method according to claim 1, characterized in that: In step 3, the optical system is optimized using a damped least squares method or an orthogonal descent method.

6. The optical-digital collaborative optimization zoom imaging system design method according to claim 1, characterized in that: The image evaluation indicators in step 3 include mean square error, peak signal-to-noise ratio, and structural similarity.

7. The optical-digital collaborative optimization zoom imaging system design method according to claim 1, characterized in that: In step 3, the image evaluation index is fed back to Zemax in the form of user operation numbers through the API interface.

8. The optical-digital collaborative optimization zoom imaging system design method according to claim 1, characterized in that: The method is applicable to short-focus, medium-focus, and long-focus scenes.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the optical-digital collaborative optimization zoom imaging system design method described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that Executable instructions are stored thereon, and when the instructions are executed by the processor, the processor can implement the optical-digital collaborative optimization zoom imaging system design method described in any one of claims 1-8.

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