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

Summary

The protocol here integrates Minimum-Preserving Subsampling with Gradient-Domain Weighted Guided Filtering to enhance the real-time dehazing capabilities of the light scattering model. Averaging the RGB values from the source image's top 0.1% brightest pixels in the dark channel produces atmospheric light, and the gradient-based Correlation Factor is used for video processing consistency.

Abstract

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.

Introduction

Haze is an atmospheric phenomenon that makes it more difficult to see distant objects when light is scattered by smoke, water droplets, or dust particles. Image deterioration due to haze is detrimental to computer vision applications^1,2,including video analysis, autonomous vehicles, and surveillance. To improve the performance of computer vision, as a first step in processing, a dehazing strategy is essential for removing haze components from images. The term "dehazing" refers to the steps used to restore clarity to a blurry or otherwise unusable image. In recent years, several techniques have been ....

Protocol

This work used synthetic or natural scene images with no human subjects involved. Therefore, no ethics approval was required.

This image dehazing protocol is developed on a standard computing setup and is designed to enhance the clarity and visibility of hazy images. The work environment is MATLAB⁷. The approach follows a systematic process involving haze estimation, refinement, and image restoration. By gradually improving image quality while preserving important details, the method delivers clear and visually appealing results. It has been tested on widely used datasets⁸ and evaluated using ....

Results

Qualitative and quantitative results provide complementary insights when evaluating a method or experiment. Qualitative results focus on subjective assessments, often using visual comparisons, perceptual evaluations, or expert opinions to analyze the effectiveness of an approach. They help illustrate improvements in real-world scenarios but can be influenced by human perception. In contrast, quantitative results rely on objective numerical metrics, such as accuracy, like NIQE¹¹, PIQE

Discussion

The proposed efficient multiscale gradient-domain filtering for image and video dehazing with an enhanced temporal coherence approach addresses the computational bottleneck in physical model-based dehazing algorithms by efficiently estimating atmospheric light and transmission maps using an image pyramid structure. The key innovation is performing MPS transmission map estimation at the coarsest pyramid level, following GWGIF filtering during up-sampling to preserve important image details. For videos, the method incorpor.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
DataSet	Vision and Image Processing Lab, University of Waterloo	5http : //ivc.uwaterloo.ca/database/Dehaze/	valuation of image and video dehazing algorithms
Gradient based weighted guided filter (Matlab implementation)	Wang  et al.	16 https://arxiv.org/pdf/2211.16796	Efficient transmission map refinement
MATLAB (with Image Processing Toolbox)	Version: MATLAB Online (24.2.0.2871072 (R2024b) Update 5)	https://www.mathworks.com/products/matlab.html	Implementation of proposed and baseline algorithms
Processor	Intel i3-6006U CPU (2.00 GHz)	https://www.intel.com/content/www/us/en/products/sku/91157/intel-core-i36006u-processor-3m-cache-2-00-ghz/specifications.html	Running algorithms
source codes for baseline methods	Kim et  al.3, Van et  al.14, Yang et al.20,  Ren et al.21,  Chen et  al.23, Li B et al.26	3https://github.com/metinsuloglu/Haze-Removal	Evaluation of learning-based dehazing methods
		14https://github.com/viengiaan/MGF dehazing
		20https://github.com/legendongary/Proximal-Dehaze-Net-CPU
		21https://github.com/rwenqi/GFN-dehazing
		23https://cchen156.github.io/code/robustdehaze.zip
		26https://github.com/Boyiliee/EVD-Net
		4 http : //live.ece.utexas.edu/research/f og/f adedef ade.html