Figures Abstract This study presents a pythagorean fuzzy set-based framework combined with contrast-limited adaptive histogram equalization (CLAHE) for low-light color image enhancement. Low-light color images often suffer from poor contrast, reduced visibility, and brightness distortion. To address these challenges, the proposed method employs pythagorean fuzzy sets to model pixel-level uncertainty more flexibly than conventional fuzzy representations. The method first transforms the input image into a pythagorean fuzzy image using a parameterized nonlinear membership mapping and then applies adaptive CLAHE to enhance contrast while preserving color fidelity. Experimental results on benchmark datasets demonstrate that the proposed approach improves visual quality and achieves competitive quantitative performance compared with existing enhancement techniques. Performance is evaluated using entropy, absolute mean brightness error, contrast improvement index, correlation coefficient, structural similarity index, and the natural image quality evaluator. The results highlight the effectiveness of integrating pythagorean fuzzy uncertainty modeling with adaptive contrast enhancement for low-light color image processing. Citation: S UM, S J (2026) Enhancing low contrast color images via pythagorean interval-valued fuzzy sets and CLAHE. PLoS One 21(8): e0354362. https://doi.org/10.1371/journal.pone.0354362 Editor: Peng Wu, Anhui University, CHINA Received: September 19, 2025; Accepted: July 7, 2026; Published: August 3, 2026 Copyright: © 2026 S., S.. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The datasets used in this study are publicly available from the Figshare repository at DOI: 10.6084/m9.figshare.27192921, and all relevant data are presented within the paper. Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. 1. Introduction Image enhancement plays a critical role in improving visual perception for computer vision, pattern recognition, and digital image