通过可微光学建模，实现镜头与图像处理的联合优化
Successive optimization of optics and post-processing with differentiable coherent PSF operator and field information
- 构建可微相干点扩散函数模型，支持复杂镜头优化
- 在多个专业级镜头上提升图像质量与光学性能
- 适合光学设计与图像重建交叉领域的研究人员
近年来，光学系统与下游算法的联合设计展现出巨大潜力。然而，现有基于光线的建模方法仅能优化几何退化，难以充分表征受波前像差或衍射效应限制的复杂微型镜头。本文提出一种精确的可微光学仿真模型，全流程操作均支持梯度传播。该模型采用新颖的初始值策略，提升高非球面表面交点计算的可靠性；同时引入可微算子，降低相干点扩散函数计算中的内存消耗。为高效应对多种退化，设计了利用波前场信息的联合优化流程。在通用复原网络引导下，该方法不仅显著提升图像质量，还在多个已达到专业水平的镜头上实现光学性能的持续优化。该联合优化框架为复杂光学系统与后处理算法的实用设计提供了新思路。源代码将公开于 https://github.com/Zrr-ZJU/Successive-optimization。
原文摘要 · Abstract (English)
Recently, the joint design of optical systems and downstream algorithms is showing significant potential. However, existing rays-described methods are limited to optimizing geometric degradation, making it difficult to fully represent the optical characteristics of complex, miniaturized lenses constrained by wavefront aberration or diffraction effects. In this work, we introduce a precise optical simulation model, and every operation in pipeline is differentiable. This model employs a novel initial value strategy to enhance the reliability of intersection calculation on high aspherics. Moreover, it utilizes a differential operator to reduce memory consumption during coherent point spread function calculations. To efficiently address various degradation, we design a joint optimization procedure that leverages field information. Guided by a general restoration network, the proposed method not only enhances the image quality, but also successively improves the optical performance across multiple lenses that are already in professional level. This joint optimization pipeline offers innovative insights into the practical design of sophisticated optical systems and post-processing algorithms. The source code will be made publicly available at https://github.com/Zrr-ZJU/Successive-optimization
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