Rainy Image

Rainy image processing focuses on removing rain artifacts from images and videos to improve the performance of computer vision systems. Current research emphasizes developing efficient and robust deep learning models, including convolutional neural networks (CNNs), transformers, and state space models (SSMs), often incorporating multi-scale architectures and attention mechanisms to handle the variability of rain patterns. These advancements aim to bridge the gap between synthetic and real-world rainy data, leading to improved accuracy in applications such as autonomous driving, surveillance, and remote sensing. The development of high-quality, diverse datasets is also a key focus, enabling more rigorous evaluation and pushing the boundaries of deraining techniques.