Successive optimization of optics and post-processing with differentiable coherent PSF operator and field information
This work addresses the problem of optimizing optical systems and algorithms for complex, miniaturized lenses, offering incremental improvements in joint design methods.
The paper tackles the joint design of optical systems and post-processing algorithms by introducing a differentiable optical simulation model with a novel initial value strategy and differential operator to handle complex lenses, resulting in enhanced image quality and improved optical performance across multiple professional-level lenses.
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