Interval-valued intuitionistic fuzzy generator based low-light enhancement model for referenced image datasets
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| Опубликовано в:: | The Artificial Intelligence Review vol. 58, no. 5 (May 2025), p. 141 |
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| Опубликовано: |
Springer Nature B.V.
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| Предметы: | |
| Online-ссылка: | Citation/Abstract Full Text - PDF |
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| Краткий обзор: | 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. |
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| ISSN: | 0269-2821 1573-7462 |
| DOI: | 10.1007/s10462-025-11138-5 |
| Источник: | ABI/INFORM Global |