Complex-domain multistage channel attention network for Fourier ptychography reconstruction

Fourier ptychography is a promising computational imaging technique that has been successfully applied in various fields, such as quantitative phase imaging, three-dimensional imaging and remote imaging. It scans a series of low-resolution images and then combines them in the Fourier domain to reconstruct a high-resolution image, achieving both wide field of view and high resolution. In this work, we report a high-fidelity Fourier ptychographic reconstruction technique based on a complex-valued channel-wise attention network (CANet) in the plug-and-play (PnP) optimization framework. Following the PnP framework, the optimization objective is decomposed into two sub-problems, including physical model constraint and statistical prior regularization. We utilize physical model constraints to ensure fidelity, and apply CANet to attenuate noise and recover fine details for the prior regularization, achieving both high fidelity and high efficiency. In the network, we introduce the channel attention module that treats amplitude and phase features as a token and calculates self-attention along the channel dimension, thereby exploiting the intrinsic relationship between amplitude and phase information. Meanwhile, we employ multi-stage channel attention modules to extract multi-resolution contextual information, improving the network’s robustness. Experiments validate that the reported technique achieves an average improvement of 1.2dB in PSNR over the compressive sensing method under different scenarios.