Adaptive Ptych: Leveraging Image Adaptive Generative Priors for Subsampled Fourier Ptychography

Fahad Shamshad, Asif Hanif, Muhammad Awais, Farwa Abbas, Ali Ahmed

Abstract: Recently pretrained generative models have shown promising results for subsampled Fourier Ptychography (FP) in terms of quality of reconstruction for extremely low sampling rates. However, the representation capabilities of these pretrained generators do not capture the full distribution for complex classes of images, such as human faces or numbers, resulting in representation error. Moreover, recent studies have shown that these pretrained generative priors struggle at high-resolution in imaging inverse problems for reconstructing a faithful estimate of the true image, potentially due to mode collapse issue. To mitigate the issue of representation error of pretrained generative models for subsampled FP, we propose to make pretrained generator image adaptive by modifying it to better represent a single image (at test time) that is consistent with the subsampled FP measurements. Our experimental results demonstrate the superiority of the proposed approach over recent subsampled FP methods in terms of both quantitative metrics and visual quality.