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

Guardado en:
Udgivet i:The Artificial Intelligence Review vol. 58, no. 5 (May 2025), p. 141
Udgivet:
Springer Nature B.V.
Fag:
Traffic surveillance
Digital imaging
Datasets
Errors
Image quality
Image enhancement
Traffic signals
Image processing
Uncertainty
Medical imaging
Signal to noise ratio
Control systems
Signal processing
Information systems
Image processing systems
Surveillance
Science and technology
Medical technology
Naturalness
Brightness
Optimization
Surveillance systems
Research applications
Biometrics
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Resumen: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.
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-025-11138-5
Fuente:ABI/INFORM Global