Opt Lett. 2021 Nov 1;46(21):5453-5456. doi: 10.1364/OL.442870.
In traditional imaging system design, the optical lens is often optimized toward the artificial optimization target like modulation transfer function and field-of-view (FoV). This usually leads to complex stacks of lenses. In order to reduce the complexity, we propose an end-to-end single lens imaging system design method. First, the imaging and processing model is established, whose input end is the ground truth image, and the output end is the restored image by Res-Unet. Then, with the optimization target of minimizing the difference between the restored image and the ground truth image, the parameters of the lens surface and the parameters of the restoration algorithm are optimized simultaneously by deep learning. In order to realize the end-to-end design, the imaging model is required to be differentiable to the lens parameters, so a fast differentiable ray tracing model is proposed. A single lens imaging system with high-quality large FoV (47°) has been designed by the end-to-end method. This method will have a wide application prospects in the design of light and small optoelectronic imaging systems.
在传统的成像系统设计中，光学透镜通常针对调制传递函数和视场（FOV）等人工优化目标进行优化。这通常会导致透镜堆叠复杂。为了降低复杂性，我们提出了一种端到端的单透镜成像系统设计方法。首先，建立成像和处理模型，其输入端是真实图像，输出端是由 Res-Unet 恢复的图像。然后，以恢复图像与真实图像之间差异最小化为优化目标，通过深度学习同时优化透镜表面参数和恢复算法参数。为了实现端到端设计，需要使成像模型对透镜参数具有可微性，因此提出了一种快速可微光线追踪模型。通过端到端方法设计了具有高质量大 FoV（47°）的单透镜成像系统。该方法在轻小型光电成像系统的设计中具有广泛的应用前景。
