4 accepted papers
Although considerable progress has been made in the deraining task under synthetic data, it is still a tough problem under real rain scenes, due to the domain gap between the synthetic and real data. Besides, difficulties in collecting and labeling diverse real rain images hinder the progress of thi…
In this work, we focus on a very practical problem: image segmentation under rain conditions. Image deraining is a classic low-level restoration task, while image segmentation is a typical high-level understanding task. Most of the existing methods intuitively employ the bottom-up paradigm by taking…
Learning-based image dehazing methods have achieved marvelous progress during the past few years. On one hand, most approaches heavily rely on synthetic data and may face difficulties to generalize well in real scenes, due to the huge domain gap between synthetic and real images. On the other hand,…
Image deraining is a typical low-level image restoration task, which aims at decomposing the rainy image into two distinguishable layers: the clean image layer and the rain layer. Most of the existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domai…