Deep learning based coherent diffraction imaging of dynamic scattering media

Abstract

The ptychographic iterative engine (PIE) is a lensless coherent diffraction imaging algorithm known for its simplicity, easy to use, scalability, and fast convergence. However, practical applications often encounter interference in imaging results caused by non-static scattering media, such as dense fog, seawater target detection and medical biology diagnosis. To address this challenge, we propose a novel approach using computational deep learning for dynamic scattering medium image reconstruction, enabling lens-free coherent diffraction imaging through dynamic scattering media. Through extensive analysis, we evaluate the effectiveness of the neural network for PIE image recovery under varying scattering medium concentration conditions. We also test scattering images obtained by hybrid training with different concentrations of scattering medium to assess the generalisation ability of the neural network. The experimental results demonstrate that our proposed method achieve PIE lens-free imaging under non-static scattering media interference. This coherent diffraction imaging method, based on transmission through dynamic scattering media, opens up new possibilities for practical applications of PIE and fosters its development in complex environments. Its significance extends to fields like atmospheric pollution, seawater target detection and medical biology diagnosis, providing valuable references for research in these domains.