Data-Driven Deep Learning for Imaging Through Scattering Media: Architectures, Learning Paradigms, and Methodological Frontiers

Abstract

Imaging through scattering media continues to be a persistent challenge in optical imaging due to the fact that scattering disrupts the direct relationship between the object and the measured signal. Once this relationship is degraded, reconstructing the original scene turns into a challenging inverse problem, for which standard imaging models are frequently insufficient. Recently, deep learning has emerged as an effective framework for reconstruction, since it allows the mapping from scattered measurements to object estimates to be learned directly from data. This review focuses particularly on purely data-driven methods, in which the neural network acts as the primary reconstruction engine instead of functioning as a supplementary element. The reviewed studies are examined in three dimensions: reconstruction frameworks, learning regimes, and system-level integration. Within this framework, we examine how various approaches trade off reconstruction accuracy, robustness, portability, and computational expense. We also consider training data requirements, adaptation strategies, generalization behavior, and evaluation practice. The literature shows clear progress toward more adaptive and robust reconstruction systems. However, several limitations continue to hinder broader applicability, including dependence on paired training data, fragmented out-of-distribution evaluation, the absence of standardized robustness and benchmarking protocols, and limited reporting on practical deployment. On the basis of this analysis, we identify several priorities for future research, encompassing the development of weakly supervised learning methodologies, the establishment of standardized multi-dimensional robustness assessment protocols, the advancement of modular physics-informed design strategies, and the exploration of more tightly integrated reconstruction frameworks.