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/.
翻译:我们建议对真实世界的降雨和清洁图像配对进行大规模数据集,并用一种方法消除图像中因雨量和雨量积累而导致的退化,因为没有关于排水排减的真正世界数据集,目前最先进的方法依赖于合成数据,因此受模拟领域差距的限制;此外,由于缺乏一个真正的对齐数据集,严格的评价仍是一项挑战。我们通过仔细控制非雨量变异,收集真实的脱排数据集,填补这一差距。我们的数据集能够对各种真实世界的降雨现象(例如雨量和雨量积)进行配对培训和定量评估。为了了解对雨现象的有力代表,我们提议建立一个深神经网络,通过尽量减少雨量和清洁图像之间的雨量-暴动损失,重建基本环境。广泛的实验表明,我们的模型在各种条件下超越了真实雨量图像上的最新脱排方法。项目网站:https://vision.ee.ucla.edu/gt_rain.htm。