Wavefront coding image reconstruction via physical prior and frequency attention

• Published in: Optics express 2023-09, Vol.31 (20), p.32875-32886
• Main Authors: Zhang, Qinghan, Bao, Meng, Sun, Liujie, Liu, Yourong, Zheng, Jihong
• Format: Article
• Language: English
• ISSN: 1094-4087; 1094-4087
• DOI: 10.1364/OE.503026

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.