A Study on the Performance of Adaptive Neural Networks for Haze Reduction with a Focus on Precision

Ahmed Alshahir, Khaled Kaaniche (), Ghulam Abbas, Paolo Mercorelli (), Mohammed Albekairi and Meshari D. Alanazi
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Ahmed Alshahir: Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia
Khaled Kaaniche: Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia
Ghulam Abbas: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Paolo Mercorelli: Institute for Production Technology and Systems (IPTS), Leuphana Universität Lüneburg, 21335 Lüneburg, Germany
Mohammed Albekairi: Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia
Meshari D. Alanazi: Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia

Mathematics, 2024, vol. 12, issue 16, 1-26

Abstract: 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.

Keywords: neural networks; machine learning; haze reduction; pixel distribution; region classification; training net (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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