Imaging inside highly scattering media using hybrid deep learning and analytical algorithm

Abstract

AbstractImaging through highly scattering media is a challenging problem with numerous applications in biomedical and remote‐sensing fields. Existing methods that use analytical or deep learning tools are limited by simplified forward models or a requirement for prior physical knowledge, resulting in blurry images or a need for large training databases. To address these limitations, we propose a hybrid scheme called Hybrid‐DOT that combines analytically derived image estimates with a deep learning network. Our analysis demonstrates that Hybrid‐DOT outperforms a state‐of‐the‐art ToF‐DOT algorithm by improving the PSNR ratio by 4.6 dB and reducing the resolution by a factor of 2.5. Furthermore, when compared to a deep learning stand‐alone model, Hybrid‐DOT achieves a 0.8 dB increase in PSNR, 1.5 times the resolution, and a significant reduction in the required dataset size (factor of 1.6–3). The proposed model remains effective at higher depths, providing similar improvements for up to 160 mean‐free paths.