Interval-valued intuitionistic fuzzy generator based low-light enhancement model for referenced image datasets

被引:1
作者
Selvam, Chithra [1 ]
Sundaram, Dhanasekar [1 ]
机构
[1] Vellore Inst Technol, Sch Adv Sci, Dept Math, Chennai 600127, Tamil Nadu, India
关键词
Low-light image enhancement; Intuitionistic fuzzy generator; Interval-valued intuitionistic fuzzy image; Structural similarity index measure; Referenced image dataset;
D O I
10.1007/s10462-025-11138-5
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Image processing is a rapidly evolving research field with diverse applications across science and technology, including biometric systems, surveillance, traffic signal control and medical imaging. Digital images taken in low-light conditions are often affected by poor contrast and pixel detail, leading to uncertainty. Although various fuzzy based techniques have been proposed for low-light image enhancement, there remains a need for a model that can manage greater uncertainty while providing better structural information. To address this, an interval-valued intuitionistic fuzzy generator is proposed to develop an advanced low-light image enhancement model for referenced image datasets. The enhancement process involves a structural similarity index measure (SSIM) based optimization approach with respect to the parameters of the generator. For experimental validation, the Low-Light (LOL), LOLv2-Real and LOLv2-Synthetic benchmark datasets are utilized. The results are compared with several existing techniques using quality metrics such as SSIM, peak signal-to-noise ratio, absolute mean brightness error, mean absolute error, root mean squared error, blind/referenceless image spatial quality evaluator and naturalness image quality evaluator, demonstrating the superiority of the proposed model. Ultimately, the model's performance is benchmarked against state-of-the-art methods, highlighting its enhanced efficiency.
引用
收藏
页数:31
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