Enhancement of Low-Light Images using Structure-Aware Illumination Mapping: A LIME Approach

Low-light image enhancement is a fundamental task in computer vision with numerous real-world applications. This paper presents a novel approach to low-light image enhancement based on the estimation of the illumination map using a Retinex-based method. Unlike traditional Retinex-based approaches, which decompose an image into reflectance and illumination components, our method focuses solely on estimating the illumination, thereby reducing computational complexity. The proposed algorithm begins by constructing an initial illumination map based on the maximum intensity of each pixel across RGB channels. Subsequently, a structure-aware smoothing technique is applied to refine the illumination map, improving its accuracy. Two algorithms are developed to solve the refinement problem: an Augmented Lagrangian Multiplier (ALM) based exact solver and a sped-up solver for reduced computational load. Experimental results demonstrate the efficacy of the proposed method, showcasing significant improvements in low-light image quality compared to state-of-the-art methods. This approach offers a promising solution for enhancing low-light images in various scenarios, with potential applications in surveillance, photography, and medical imaging.