Abstract

Low-light image enhancement techniques enable better visual quality by showing hidden structural details that become visible through their application. Traditional contrast enhancement methods that improve visibility create challenges because they alter fundamental image structures while increasing noise levels. The trainable Intuitionistic Fuzzy Generator (TIFG) serves as solution for this particular situation because it improves the quality of dimly illuminated images. The proposed technique establishes an intuitionistic fuzzy set framework to model uncertainty about low-light conditions through a specific improvement process. Unlike traditional fuzzy generators with fixed complements, TIFG employs trainable membership, non-membership, and hesitation functions, allowing adaptive learning of enhancement parameters. To provide stability across a range of illumination levels, these parameters are tuned using the Adam optimizer and a custom loss function. The process uses Contrast Limited Adaptive Histogram Equalization (CLAHE) to achieve two objectives that include reducing noise amplification and improving local contrast. The TIFG+CLAHE method establishes its superior visual quality and quantitative performance through its experiments conducted on the Low-Light (LoLI), Ex- Dark, and MIT-5K datasets that show its strength against traditional enhancement methods. Therefore, the results demonstrate that the TIFG+CLAHE method is an effective framework that adapts to different image enhancement scenarios.

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