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 Rem...
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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.
ABSTRACTImages 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 ...
详细信息
ABSTRACTImages 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.
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