Analytical-Heuristic Modeling and Optimization for Low-Light Image Enhancement
This work addresses the problem of improving visibility in low-light images for computer vision applications, representing an incremental advance through hybrid optimization.
The paper tackled low-light image enhancement by combining genetic algorithms with analytical models to transform dark images into visible ones, achieving top ranking among 26 state-of-the-art algorithms on the LOL benchmark.
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. Genetic algorithms are part of metaheuristic approaches, which proved helpful in solving challenging optimization tasks. We propose two analytical methods 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 proposed approach ranks at the top among 26 state-of-the-art algorithms in the LOL benchmark. 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 swarm and evolutionary computation community and others interested in analytical and heuristic reasoning.