首页|Video enhancement for dense haze removal using an optimized multi-task evolutionary artificial neural network

Video enhancement for dense haze removal using an optimized multi-task evolutionary artificial neural network

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Images captured during cloudy or misty weather often suffer from poor contrast, colour distortions, and limited visibility. To overcome these challenges, this manuscript proposes a Video Enhancement for Dense Haze Removal using an Optimized Multi-Task Evolutionary Artificial Neural Network (VE-MTEANN-BWOA-DHR). Initially, input videos are sourced from the Real Haze Video Database. Then Adaptive Self-Guided Filtering (ASGF) for eliminate noise from the input video. Then Ternary Pattern with Discrete Wavelet Transform (TPDWT) is used to extract the features. The extracted features are given to a Multi-Task Evolutionary Artificial Neural Network (MTEANN) to classify the dense haze levels in video frames as hazy image 1, hazy image 2, hazy image 3, and hazy image 4. Typically, MTEANN lacks adaptive optimization strategies to determine ideal parameters for effective video enhancement. Therefore, the Beluga Whale Optimization Algorithm (BWOA) is utilized to optimize MTEANN. The proposed VE-MTEANN-BWOA-DHR method demonstrates superior performance compared to the existing models.

Adaptive self-guided filteringBeluga Whale Optimization AlgorithmMulti-Task Evolutionary Artificial Neural NetworkTernary pattern with discrete wavelet transformvideo enhancementHaze ClassificationDense Haze RemovalComputer Vision

Vishalkirthi S. Patil、Raviraj H. Havaldar

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Department of Electronics and Communication Engineering, Angadi Institute of Technology and Management, Belagavi, India

Department of Biomedical Engineering, KLE Technological University's DR. M. S. Sheshgiri College of Engineering & Technology, Udyambag, India

2025

10.1080/13682199.2024.2364124

The imaging science journal

The imaging science journal

ISSN：1368-2199
年,卷(期)：2025.73(3/4)
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