A Method of Removing Rain or Snow from A Color Image using MATLAB

Abstract

Background: Poor weather conditions, such as rain or snow, may seriously degrade the quality of color photographs. This deterioration may impact various domains, such as surveillance systems, outdoor visibility, and image-based analysis in areas such as computer vision and remote sensing. Objective: The main aim of this study is to offer a unique approach for rapidly removing rain or snow from color photographs using MATLAB while retaining the image's original quality and features. Methods: This article uses the L0 gradient minimization approach to target and eliminate rain pixels. Rather than relying simply on local features, this global technique finds and retains critical edges across the picture. After removing the rain, the picture quality is improved further by modifying the histogram to boost contrast. The approach incorporates color correction and enhancement methods inspired by many sources to ensure accuracy in the rain or snow removal procedure. Results: The results show this strategy works well, even in severe rain. It not only eliminates rain successfully, but it also keeps important picture features. However, thorough removal may demand reducing overall picture quality in severe rain or snow. Conclusion: The suggested approach may provide a viable option for effectively eliminating rain or snow from color photographs, improving visual interpretation and analysis in various applications. This strategy significantly advances image processing and computer vision by addressing the problems caused by poor picture quality due to severe weather conditions

Keywords

• removing rain
• still photographs
• raindrops
• l0 gradient minimization
• non-zero gradients
• global scale
• important edges
• histogram modification
• boost contrast
• efficient rain removal.