Phys.org   October 30, 2023 The existing deep-learning methods for holographic imaging often depend solely on the specific condition based on the given data distributions. One critical problem is how to guarantee the alignment between any given downstream tasks and pretrained models. Researchers in China analyzed the physical mechanism of image degradation caused by turbulence and proposed a swin transformer-based method, termed train-with-coherence-swin (TWC-Swin) transformer, which used spatial coherence (SC) as an adaptable physical prior information to precisely align image restoration tasks in the arbitrary turbulent scene. They designed light-processing system (LPR) which enabled manipulation of SC and simulation of any […]