An Adaptive Dark Region Detail Enhancement Method for Low-light Images

Abstract

The images captured in low-light conditions are often of poor visual quality as most of details in dark regions buried. Although some advanced low-light image enhancement methods could lighten an image and its dark regions, they still cannot reveal the details in dark regions very well. This paper presents an adaptive dark region detail enhancement method for low-light images. As our method is based on the Retinex theory, we first formulate the Retinex-based low-light image enhancement problem into a Bayesian optimization framework. Then, a dark region prior is proposed and an adaptive gradient amplification strategy is designed to incorporate this prior into the illumination estimation. The dark region prior, together with the widely used spatial smooth and structure priors, leads to a dark region and structure-aware smoothness regularization term for illumination optimization. We provide a solver to this optimization and get final enhanced results after post processing. Experiments demonstrate that our method can obtain good enhancement results with better dark region details compared to several state-of-the-art methods.