Interval-valued intuitionistic fuzzy generator based low-light enhancement model for referenced image datasets
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| 出版年: | The Artificial Intelligence Review vol. 58, no. 5 (May 2025), p. 141 |
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| 出版事項: |
Springer Nature B.V.
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text - PDF |
| タグ: |
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MARC
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| 001 | 3170746225 | ||
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| 022 | |a 0269-2821 | ||
| 022 | |a 1573-7462 | ||
| 024 | 7 | |a 10.1007/s10462-025-11138-5 |2 doi | |
| 035 | |a 3170746225 | ||
| 045 | 2 | |b d20250501 |b d20250531 | |
| 084 | |a 68693 |2 nlm | ||
| 245 | 1 | |a Interval-valued intuitionistic fuzzy generator based low-light enhancement model for referenced image datasets | |
| 260 | |b Springer Nature B.V. |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Traffic surveillance | ||
| 653 | |a Digital imaging | ||
| 653 | |a Datasets | ||
| 653 | |a Errors | ||
| 653 | |a Image quality | ||
| 653 | |a Image enhancement | ||
| 653 | |a Traffic signals | ||
| 653 | |a Image processing | ||
| 653 | |a Uncertainty | ||
| 653 | |a Medical imaging | ||
| 653 | |a Signal to noise ratio | ||
| 653 | |a Control systems | ||
| 653 | |a Signal processing | ||
| 653 | |a Information systems | ||
| 653 | |a Image processing systems | ||
| 653 | |a Surveillance | ||
| 653 | |a Science and technology | ||
| 653 | |a Medical technology | ||
| 653 | |a Naturalness | ||
| 653 | |a Brightness | ||
| 653 | |a Optimization | ||
| 653 | |a Surveillance systems | ||
| 653 | |a Research applications | ||
| 653 | |a Biometrics | ||
| 773 | 0 | |t The Artificial Intelligence Review |g vol. 58, no. 5 (May 2025), p. 141 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3170746225/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3170746225/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |