Enhanced Rain Streak Removal from Images Using GFRU-Net

This study introduces a sophisticated approach for eliminating rain streaks from static images by employing a tailored GFRU-Net model. The process commences by employing guided filtering to preprocess the image affected by rain, which is then followed by the initial identification of rain pixels. The GFRU-Net model utilizes advanced techniques such as enhanced encoder and decoder paths, skip connections, and attention mechanisms to effectively eliminate rain streaks from images while maintaining image details. The method’s effectiveness is demonstrated through quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR) and rain removal percentage. This evaluation shows significant enhancements in image clarity and quality.