Didrpg2emtl_comp.rar -

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

Settings for hyperparameters and directory paths used during the "comp" (computation/comparison) phase of the research. Performance and Impact DIDRPG2EMTL_comp.rar

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. The network focuses on learning the "rain residual"

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. Instead of attempting to remove all rain in

Python implementation (often using PyTorch or TensorFlow).

.pth or .ckpt files that allow users to run the de-rain algorithm without training from scratch.

The DID-RPG approach is notable for achieving a high and Structural Similarity Index (SSIM) compared to older methods like DDN (Deep Detail Network). It effectively preserves the background textures while removing both heavy and light rain streaks.