Rain Generation

Rain generation is a rapidly developing field focused on creating realistic synthetic rain in images, primarily to augment datasets for training computer vision models that process rainy images (e.g., deraining algorithms, object detectors). Current research emphasizes developing deep learning-based generators that incorporate physical rain characteristics (e.g., drop shape, density) for better control and realism, often employing techniques like contrastive learning and novel loss functions to minimize artifacts. These advancements improve the performance and generalization of downstream tasks by providing high-quality, diverse training data, ultimately leading to more robust and accurate image processing in challenging weather conditions.