TurbFPNet: neural far-field turbulent Fourier ptychography with a camera array
Rui Zhou, Y. Y. Guo, Qi Zhang, Mingbo Pu, Yuran Lu, Yingjie Shang, Xiaoyin Li, Fei Zhang, Mingfeng Xu, Xiangang Luo
Abstract
Although phase retrieval techniques empower macroscopic Fourier ptychography (FP) to boost spatial resolution, such iterative methods need indispensable data redundancy and tend to be time-consuming for image acquisition and reconstruction and are prone to ill-posedness because of the entanglement of pixel displacement, blur, and noise when increasing the imaging distance. This work proposes a turbulent Fourier ptychographic network (TurbFPNet), a physics-based neural framework designed to address these challenges. In contrast to recently proposed methods, we investigate implicit neural representations with optics processing steps, termed Fourier ptychographic phase-to-space (FP-P2S) transform for data synthesis based on a real coherent imaging system, where real sub-aperture images are captured by an assembled camera array, mitigating the scarcity of training and testing coherent data with turbulence. In addition, we present a pyramid residual transformer to extract both coarse-grained and fine-grained features by taking advantage of convolutional neural networks and transformers. We further introduce the FP reconstruction loss to obtain a desired underlying mapping by making the best of our FP-P2S to produce extra sub-aperture images. Experimental results indicate that our TurbFPNet surpasses the recent baselines in performance. This study tackles the challenges, including turbulence, negative overlap ratio, and speckle, together for single-shot far-field FP imaging, making macroscopic FP more applicable for far-field scenarios.