Compounded Layer-Prior Unrolling: A Unified Transmission-Based Image Enhancement Framework

Improving the quality of images degraded by various transmission media has important practical significance. Such enhancement tasks involve resolving both transmission degradation and residual contamination including imaging noise, color distortion, and occlusions. Existing methods typically develop the priors on natural scenes to resolve ill-posed problems separately. However, the solutions derived from hand-crafted priors may fail on specific regions where a priori assumptions break, and recent data-driven methods highly depend on training data owing to the absence of effective priors. Based on a unified formulation for transmission-based image enhancement tasks, we develop a compounded unrolling framework to generate hybrid image layer propagations. Specifically, as multiple deeply-trained priors are integrated into the iterative propagation scheme, the deep model can recognize specific task properties and data distributions for different applications. Both quantitative and qualitative experiments demonstrate the superior performance of the proposed framework on various transmission-based tasks (haze removal, underwater image enhancement and rain removal).