论文标题
迈向现实世界的单图像：一个新的基准测试和超越
Toward Real-world Single Image Deraining: A New Benchmark and Beyond
论文作者
论文摘要
近年来，在实际场景中，单图像（SID）引起了越来越多的关注。由于难以获得真实世界/清洁图像对，因此以前的真实数据集遭受了低分辨率图像，均匀的雨条，背景变化有限，甚至对图像对的不对准，从而对SID方法进行了不可思议的评估。为了解决这些问题，我们建立了一个名为Realrain-1K的新的高质量数据集，该数据集分别由1,120美元的高分辨率配对的清洁和高雨图像组成，分别具有低密度和高密度降雨条纹。 Realrain-1K中的图像是通过简单但有效的可控制降雨密度控制的过滤方法自动从大量实际的雨视频剪辑中生成的，并且具有高图像分辨率，背景多样性，多样性的多样性和严格的空间对齐的良好特性。 Realrain-1K还提供丰富的雨条层作为副产品，使我们能够通过将雨条层粘贴在丰富的自然图像上，构建一个名为Synrain-13K的大规模合成数据集。基于它们和现有数据集，我们在三个曲目上基准了10种代表性的SID方法：（1）对Realrain-1k的全面监督学习，（2）域对真实数据集的概括，以及（3）SYN-to-eal To-真实传输学习。实验结果（1）显示了图像恢复性能和模型复杂性中代表性方法的差异，（2）验证所提出的数据集在模型概括中的重要性，（3）提供了有关从不同领域学习的优越性的有用见解，并在现实世界中对未来的研究进行了研究。数据集将在https://github.com/hiker-lw/realrain-1k上发布
Single image deraining (SID) in real scenarios attracts increasing attention in recent years. Due to the difficulty in obtaining real-world rainy/clean image pairs, previous real datasets suffer from low-resolution images, homogeneous rain streaks, limited background variation, and even misalignment of image pairs, resulting in incomprehensive evaluation of SID methods. To address these issues, we establish a new high-quality dataset named RealRain-1k, consisting of $1,120$ high-resolution paired clean and rainy images with low- and high-density rain streaks, respectively. Images in RealRain-1k are automatically generated from a large number of real-world rainy video clips through a simple yet effective rain density-controllable filtering method, and have good properties of high image resolution, background diversity, rain streaks variety, and strict spatial alignment. RealRain-1k also provides abundant rain streak layers as a byproduct, enabling us to build a large-scale synthetic dataset named SynRain-13k by pasting the rain streak layers on abundant natural images. Based on them and existing datasets, we benchmark more than 10 representative SID methods on three tracks: (1) fully supervised learning on RealRain-1k, (2) domain generalization to real datasets, and (3) syn-to-real transfer learning. The experimental results (1) show the difference of representative methods in image restoration performance and model complexity, (2) validate the significance of the proposed datasets for model generalization, and (3) provide useful insights on the superiority of learning from diverse domains and shed lights on the future research on real-world SID. The datasets will be released at https://github.com/hiker-lw/RealRain-1k
