2018/06/20 by Yu Sun, Sun, Yu, Ulugbek S. Kamilov +1
Engineering · Physics and Astronomy · #Computer Vision and Pattern Recognition (cs.CV) #Digital Holography and Microscopy #FOS: Computer and information sciences #Optical Coherence Tomography Applications #Photonic and Optical Devices
paper · pdf · doi:10.48550/arxiv.1806.08015
openalex publication_date 2018/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The problem of image reconstruction under multiple light scattering is usually formulated as a regularized non-convex optimization. A deep learning architecture, Scattering Decoder (ScaDec), was recently proposed to solve this problem in a purely data-driven fashion. The proposed method was shown to substantially outperform optimization-based baselines and achieve state-of-the-art results. In this paper, we thoroughly test the robustness of ScaDec to different permittivity contrasts, number of transmissions, and input signal-to-noise ratios. The results on high-fidelity simulated datasets show that the performance of ScaDec is stable in different settings.