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 the first 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 invariant to rain phenomena, we propose a deep neural network that reconstructs the underlying scene by minimizing a rain-invariant loss between rainy and clean images. Extensive experiments demonstrate that the proposed dataset benefits existing derainers, and our model can outperform the state-of-the-art deraining methods on real rainy images under various conditions.
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,并用一种方法从图像中去除由降雨量和雨水积累引起的退化。由于目前没有关于排水量减少的真实世界数据集,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,由于缺乏一个真实的对齐数据集,严格的评价仍是一项挑战。我们通过仔细控制非降雨变异,收集第一个真实的脱排数据集,填补了这一差距。我们的数据集使得对不同的真实世界降雨现象(如降雨量和雨水积累)进行配对培训和数量评估成为可能。为了了解降雨现象的变数,我们提议了一个深神经网络,通过尽量减少降雨和清洁图像之间的降雨变化损失,重建基本环境。广泛的实验表明,拟议的数据集有利于现有的脱雨者,而我们的模型可以超越各种条件下对实际雨季图像采用的最新脱雨方法。