Wavefront coding image reconstruction via physical prior and frequency attention
Qinghan Zhang, Meng Bao, Liujie Sun, Yourong Liu, Jihong Zheng
Abstract
Wavefront coding (WFC) is an effective technique for extending the depth-of-field of imaging systems, including optical encoding and digital decoding. We applied physical prior information and frequency domain model to the wavefront decoding, proposing a reconstruction method by a generative model. Specifically, we rebuild the baseline inspired by the transformer and propose three modules, including the point spread function (PSF) attention layer, multi-feature fusion block, and frequency domain self-attention block. These models are used for end-to-end learning to extract PSF feature information, fuse it into the image features, and further re-normalize the image feature information, respectively. To verify the validity, in the encoding part, we use the genetic algorithm to design a phase mask in a large field-of-view fluorescence microscope system to generate the encoded images. And the experimental results after wavefront decoding show that our method effectively reduces noise, artifacts, and blur. Therefore, we provide a deep-learning wavefront decoding model, which improves reconstruction image quality while considering the large depth-of-field (DOF) of a large field-of-view system, with good potential in detecting digital polymerase chain reaction (dPCR) and biological images.