A Study on the Performance of Adaptive Neural Networks for Haze Reduction with a Focus on Precision
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In: Mathematics, Vol. 12, No. 16, 2526, 15.08.2024.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - A Study on the Performance of Adaptive Neural Networks for Haze Reduction with a Focus on Precision
AU - Alshahir, Ahmed
AU - Kaaniche, Khaled
AU - Abbas, Ghulam
AU - Mercorelli, Paolo
AU - Albekairi, Mohammed
AU - Alanazi, Meshari D.
N1 - Publisher Copyright: © 2024 by the authors.
PY - 2024/8/15
Y1 - 2024/8/15
N2 - Visual clarity is significantly compromised, and the efficacy of numerous computer vision tasks is impeded by the widespread presence of haze in images. Innovative approaches to accurately minimize haze while keeping image features are needed to address this difficulty. The difficulties of current methods and the need to create better ones are brought to light in this investigation of the haze removal problem. The main goal is to provide a region-specific haze reduction approach by utilizing an Adaptive Neural Training Net (ANTN). The suggested technique uses adaptive training procedures with external haze images, pixel-segregated images, and haze-reduced images. Iteratively comparing spectral differences in hazy and non-hazy areas improves accuracy and decreases haze reduction errors. This study shows that the recommended strategy significantly improves upon the existing training ratio, region differentiation, and precision methods. The results demonstrate that the proposed method is effective, with a 9.83% drop in mistake rate and a 14.55% drop in differentiating time. This study’s findings highlight the value of adaptable neural networks for haze reduction without losing image quality. The research concludes with a positive outlook on the future of haze reduction methods, which should lead to better visual clarity and overall performance across a wide range of computer vision applications.
AB - Visual clarity is significantly compromised, and the efficacy of numerous computer vision tasks is impeded by the widespread presence of haze in images. Innovative approaches to accurately minimize haze while keeping image features are needed to address this difficulty. The difficulties of current methods and the need to create better ones are brought to light in this investigation of the haze removal problem. The main goal is to provide a region-specific haze reduction approach by utilizing an Adaptive Neural Training Net (ANTN). The suggested technique uses adaptive training procedures with external haze images, pixel-segregated images, and haze-reduced images. Iteratively comparing spectral differences in hazy and non-hazy areas improves accuracy and decreases haze reduction errors. This study shows that the recommended strategy significantly improves upon the existing training ratio, region differentiation, and precision methods. The results demonstrate that the proposed method is effective, with a 9.83% drop in mistake rate and a 14.55% drop in differentiating time. This study’s findings highlight the value of adaptable neural networks for haze reduction without losing image quality. The research concludes with a positive outlook on the future of haze reduction methods, which should lead to better visual clarity and overall performance across a wide range of computer vision applications.
KW - haze reduction
KW - machine learning
KW - neural networks
KW - pixel distribution
KW - region classification
KW - training net
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85202520636&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/997e5f4d-a7c9-3d08-a05c-e7d4be916a81/
U2 - 10.3390/math12162526
DO - 10.3390/math12162526
M3 - Journal articles
AN - SCOPUS:85202520636
VL - 12
JO - Mathematics
JF - Mathematics
SN - 2227-7390
IS - 16
M1 - 2526
ER -