PSO-based parameter optimization of intuitionistic fuzzy generator for low-light image enhancement

Low-light images often suffer from reduced visibility, noise, and loss of structural details due to insufficient illumination and sensor limitations. These degradations affect both visual perception and downstream image analysis tasks. This paper presents a low-light image enhancement framework based on intuitionistic fuzzy generator (IFG) integrated with gamma correction and optimized using particle swarm optimization (PSO). As a preprocessing step, block-matching and 3D filtering (BM3D) are applied to suppress noise while preserving structural information. The IFG models uncertainty in pixel intensities to enable adaptive contrast enhancement, whereas gamma correction adjusts brightness levels. The enhancement parameters are optimized using PSO guided by dataset-specific objective functions, namely structural similarity (SSIM) for reference datasets and entropy-based optimization for no-reference scenarios where ground-truth images are unavailable. Experimental evaluations on standard benchmark datasets using both reference and no-reference image quality metrics indicate that the proposed framework achieves competitive enhancement performance with improved contrast and preservation of visually relevant image details. Although the computational cost is higher than that of feed-forward deep learning models, the framework is suitable for applications where training data are unavailable and interpretable parameter-adaptive enhancement is preferred.