Code to run the de-rainer on the provided sample "Rain200L" or "Rain200H" datasets.
Instead of attempting to remove all rain in a single step, the model decomposes the rain layer into multiple stages. It progressively removes rain streaks by grouping them based on their physical characteristics. DIDRPG2EMTL_comp.rar
The paper addresses the challenge of removing rain streaks from single images (de-raining) by introducing a recurrent framework that handles rain streaks of varying densities and shapes. Code to run the de-rainer on the provided
The network focuses on learning the "rain residual" (the difference between the rainy image and the clean background), making the training process more stable and effective. Content of the .rar File The paper addresses the challenge of removing rain
The primary research paper associated with this file is authored by Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng , typically presented at major computer vision conferences like CVPR (Conference on Computer Vision and Pattern Recognition). Key Technical Contributions
The architecture uses recurrence to reuse parameters across different stages of the de-raining process, which reduces the model size while improving its ability to handle complex rain patterns.