We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image. As there exists no real-world dataset for deraining, current state-of-the-art methods rely on synthetic data and thus are limited by the sim2real domain gap; moreover, rigorous evaluation remains a challenge due to the absence of a real paired dataset. We fill this gap by collecting a real paired deraining dataset through meticulous control of non-rain variations. Our dataset enables paired training and quantitative evaluation for diverse real-world rain phenomena (e.g. rain streaks and rain accumulation). To learn a representation robust to rain phenomena, we propose a deep neural network that reconstructs the underlying scene by minimizing a rain-robust loss between rainy and clean images. Extensive experiments demonstrate that our model outperforms the state-of-the-art deraining methods on real rainy images under various conditions. Project website: https://visual.ee.ucla.edu/gt_rain.htm/.
@article{arxiv.2206.10779,
title = {Not Just Streaks: Towards Ground Truth for Single Image Deraining},
author = {Yunhao Ba and Howard Zhang and Ethan Yang and Akira Suzuki and Arnold Pfahnl and Chethan Chinder Chandrappa and Celso de Melo and Suya You and Stefano Soatto and Alex Wong and Achuta Kadambi},
journal= {arXiv preprint arXiv:2206.10779},
year = {2024}
}