A New Regularization for Retinex Decomposition of Low-Light Images

We study unsupervised Retinex decomposition for low light image enhancement. Being an underdetermined problem with infinite solutions, well-suited priors are required to reduce the solution space. In this paper, we analyze the characteristics of low-light images and their illumination component and identify a trivial solution not taken into consideration by the previous unsupervised state-of-the-art methods. The challenge comes from the fact that the trivial solution cannot be completely eliminated from the feasible set as it corresponds to the true solution when the low-light image contains a light source or an overexposed area. To address this issue, we propose a new regularization term which only remove absurd solutions and keep plausible ones in the set. To demonstrate the efficiency of the proposed prior, we conduct our experiments using deep image priors in a framework similar to the recent work RetinexDIP and an in-depth ablation study. Finally, we observe no more halo artefacts in the restored image. For all-but-one metrics, our unsupervised approach gives results as good as the supervised state-of-the-art indicating the potential of this framework for low-light image enhancement.