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This paper presents a novel and interpretable end-to-end learning framework,called the deep compensation unfolding network (DCUNet), for restoring lightfield (LF) images captured under low-light conditions. DCUNet is designed witha multi-stage architecture that mimics the optimization process of solving aninverse imaging problem in a data-driven fashion. The framework uses theintermediate enhanced result to estimate the illumination map, which is thenemployed in the unfolding process to produce a new enhanced result.Additionally, DCUNet includes a content-associated deep compensation module ateach optimization stage to suppress noise and illumination map estimationerrors. To properly mine and leverage the unique characteristics of LF images,this paper proposes a pseudo-explicit feature interaction module thatcomprehensively exploits redundant information in LF images. The experimentalresults on both simulated and real datasets demonstrate the superiority of ourDCUNet over state-of-the-art methods, both qualitatively and quantitatively.Moreover, DCUNet preserves the essential geometric structure of enhanced LFimages much better. The code will be publicly available athttps://github.com/lyuxianqiang/LFLL-DCU.

Enhancing Low-light Light Field Images with A Deep Compensation Unfolding Network