Illumination estimation for nature preserving low-light image enhancement

In retinex model, images are considered as a combination of two components: illumination and reflectance. However, decomposing an image into the illumination and reflectance is an ill-posed problem. This paper presents a new approach to estimate the illumination for low-light image enhancement. This work contains three major tasks: estimation of structure-aware initial illumination, refinement of the estimated illumination, and the final correction of lightness in refined illumination. We have proposed a novel approach for structure-aware initial illumination estimation leveraging a new multi-scale guided filtering approach. The algorithm refines proposed initial estimation by formulating a new multi-objective function for optimization. Further, we proposed a new adaptive illumination adjustment for correction of lightness using the estimated illumination. The qualitative and quantitative analysis on low-light images with varying illumination shows that the proposed algorithm performs image enhancement with color constancy and preserves the natural details. The performance comparison with state-of-the-art algorithms shows the superiority of the proposed algorithm.