Images captured in low-light conditions are prone to suffer from low visibility, which may further degrade the performance of most computational photography and computer vision applications. In this paper, we propose a low-light image degradation model derived from the atmospheric scattering model, which is simple but effective and robust. Then, we present a physically valid image prior named pure pixel ratio prior, which is a statistical regularity of extensive nature clear images. Based on the proposed model and the image prior, a corresponding low-light image enhancement method is also presented. In this method, we first segment the input image into scenes according to the brightness similarity and utilize a high-efficiency scene-based transmission estimation strategy rather than the traditional per-pixel fashion. Next, we refine the rough transmission map, by using a total variation smooth operator, and obtain the enhanced image accordingly. Experiments on a number of challenging nature low-light images verify the effectiveness and robustness of the proposed model, and the corresponding method can show its superiority over several state of the arts.
Gu, Z., Chen, C., & Zhang, D. (2018). A low-light image enhancement method based on image degradation model and pure pixel ratio prior. Mathematical Problems in Engineering, 2018. https://doi.org/10.1155/2018/8178109