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

Y1 - 2024/8

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

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 -