Efficient Multiscale Gradient-Domain Filtering for Image and Video Dehazing with Enhanced Temporal Coherence

Dehazing is crucial in computer vision to restore image clarity affected by atmospheric scattering. Existing methods suffer from high computational costs, loss of gradient details, and flickering artifacts in video applications. To enhance efficiency and visual quality, this work proposes a multiscale gradient-domain weighted guided image filter-based dehazing technique applicable to both videos and images. To estimate atmospheric parameters and reduce computational complexity, Minimum Preserving Subsampling (MPS) has been employed. Next, an iterative up-sampling process with the Gradient-domain Weighted Guided Image Filter (GWGIF) refines the transmission map, preserving a significant amount of gradient features and thereby enhancing texture and edge retention. For video dehazing, the Gradient-Based Correlation Factor (GCF) is introduced, resulting in a significant reduction in flickering artifacts compared to existing methods. Experimental evaluations demonstrate the superiority of our approach, achieving a Perception-based Image Quality Evaluator (PIQE) score of 26.98, a Natural Image Quality Evaluator (NIQE) score of 2.78, and a Blind/Referenceless Image Spatial Quality Evaluator (BRISQE) score of 20.18, reflecting improved perceptual quality. Furthermore, the proposed method ensures high temporal coherence in video dehazing, with Mean Square Error (MSE) deviation of 0.003, making it ideal for real-time applications such as autonomous vehicles, surveillance, and remote sensing.