Successive optimization of optics and post-processing with differentiable coherent PSF operator and field information

TL;DR

This paper presents a differentiable optical simulation model and a joint optimization method that enhances both optical design and image post-processing, enabling successive improvements in complex lens systems.

Contribution

It introduces a novel differentiable optical simulation with a new initial value strategy and a joint optimization framework leveraging field information for complex lens design.

Findings

Improved optical simulation accuracy for complex lenses.

Enhanced image quality through joint optimization.

Successive optical performance improvements across multiple lenses.

Abstract

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