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

翻译：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，现有的基于光线描述的方法仅限于优化几何退化，难以完整表征受波前像差或衍射效应制约的复杂微型化镜头的光学特性。本研究引入了一种精确的光学仿真模型，其流程中的每个操作均是可微分的。该模型采用一种新颖的初值策略，以提高在高次非球面上进行交点计算的可靠性。此外，它利用微分算子来降低相干点扩散函数计算过程中的内存消耗。为了有效应对各类退化，我们设计了一种利用场信息的联合优化流程。在通用复原网络的指导下，所提方法不仅提升了图像质量，还能对已达到专业水平的多个镜头的光学性能进行逐次改进。这一联合优化流程为复杂光学系统与后处理算法的实用化设计提供了创新思路。源代码将在 https://github.com/Zrr-ZJU/Successive-optimization 公开。