Analytical-heuristic modeling and optimization for low-light image enhancement

Low-light image enhancement remains an open problem, and the new wave of artificial intelligence is at the center of this problem. This work describes the use of genetic algorithms for optimizing analytical models that can improve the visualization of images with poor light. The main goal of low-light image enhancement is to produce an image with features similar to those of a well-taken photograph under optimal lighting conditions. We propose a balanced analytical-heuristic method combined with optimization reasoning to approach a solution to the physical and computational aspects of transforming dark images into visible ones. The experiments demonstrate that the dichotomy-tuned approach ranks at the top among 26 state-of-the-art algorithms in the LOL (LOw-Light) benchmark, reaching a staggering 27.1717 in the synthetic version LOLv2. Moreover, the proposed dichotomy-tuned algorithm provides a pleasant visual appearance with room for improvement. The results show evidence that a simple genetic algorithm combined with analytical reasoning can defeat the current mainstream in a challenging computer vision task through controlled experiments and objective comparisons. This work opens interesting new research avenues for the soft computing community and others interested in analytical and heuristic reasoning.