Low contrast enhancement algorithm for color image using pythagorean fuzzy sets with a fusion of CLAHE and BPDHE methods

This paper presents a novel low-contrast enhancement algorithm for color images based on Pythagorean fuzzy sets (PFS) integrated with a fusion of CLAHE (Contrast Limited Adaptive Histogram Equalization) and BPDHE (Brightness Preserving Dynamic Histogram Equalization) techniques. The proposed method aims to effectively enhance image contrast while maintaining brightness and preserving natural details. Initially, the input image is normalized and transformed into a Pythagorean fuzzy domain, where membership, non-membership, and hesitation degrees are computed to better model uncertainty and image ambiguity. The resulting Pythagorean fuzzy image is then converted into an interval-valued intuitionistic fuzzy representation to enhance local and global contrast adaptively. CLAHE and BPDHE methods are independently applied to this fuzzy-transformed image, capturing both localized contrast and global brightness features. A weighted fusion strategy combines these enhanced images (0.7 × BPDHE + 0.3 × CLAHE) to produce a visually balanced result. Finally, the fused image is mapped back to the spatial domain to generate the enhanced output. Experimental results demonstrate that the proposed algorithm effectively improves image visibility and contrast without introducing artifacts or color distortions, outperforming conventional enhancement techniques.

Keywords: Low-contrast enhancement, Pythagorean fuzzy sets, CLAHE, BPDHE, image fusion, brightness preservation, histogram equalization, interval-valued intuitionistic fuzzy image, contrast enhancement, color image processing.