Entropy-Driven Genetic Optimization for Deep-Feature-Guided Low-Light Image Enhancement

TL;DR

This paper introduces an unsupervised, fuzzy-inspired image enhancement framework guided by a multi-objective genetic algorithm, which optimizes brightness, contrast, and gamma to improve low-light images while preserving semantic content.

Contribution

It presents a novel unsupervised approach combining deep features and multi-objective optimization for low-light image enhancement, without requiring paired training data.

Findings

Achieves state-of-the-art BRISQUE and NIQE scores on unpaired datasets.

Enhances shadowed regions with improved visibility and detail.

Maintains semantic fidelity and natural appearance without artifacts.

Abstract

Image enhancement methods often prioritize pixel level information, overlooking the semantic features. We propose a novel, unsupervised, fuzzy-inspired image enhancement framework guided by NSGA-II algorithm that optimizes image brightness, contrast, and gamma parameters to achieve a balance between visual quality and semantic fidelity. Central to our proposed method is the use of a pre trained deep neural network as a feature extractor. To find the best enhancement settings, we use a GPU-accelerated NSGA-II algorithm that balances multiple objectives, namely, increasing image entropy, improving perceptual similarity, and maintaining appropriate brightness. We further improve the results by applying a local search phase to fine-tune the top candidates from the genetic algorithm. Our approach operates entirely without paired training data making it broadly applicable across domains with limited or noisy labels. Quantitatively, our model achieves excellent performance with average BRISQUE and NIQE scores of 19.82 and 3.652, respectively, in all unpaired datasets. Qualitatively, enhanced images by our model exhibit significantly improved visibility in shadowed regions, natural balance of contrast and also preserve the richer fine detail without introducing noticable artifacts. This work opens new directions for unsupervised image enhancement where semantic consistency is critical.