Not Just Streaks: Towards Ground Truth for Single Image Deraining

• URL: http://arxiv.org/abs/2206.10779v3
• Date: Mon, 29 Jul 2024 16:28:41 GMT
• Title: Not Just Streaks: Towards Ground Truth for Single Image Deraining
• Authors: Yunhao Ba, Howard Zhang, Ethan Yang, Akira Suzuki, Arnold Pfahnl, Chethan Chinder Chandrappa, Celso de Melo, Suya You, Stefano Soatto, Alex Wong, Achuta Kadambi,
• Abstract summary: We propose a large-scale dataset of real-world rainy and clean image pairs. We propose a deep neural network that reconstructs the underlying scene by minimizing a rain-robust loss between rainy and clean images.
• Score: 42.15398478201746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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