From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal
This addresses the data bottleneck for researchers in image deraining, though it is incremental by focusing on dataset creation and a baseline method.
The authors tackled the lack of large-scale high-quality paired training data for real image deraining by constructing LHP-Rain, a benchmark with 3000 video sequences and 1 million high-resolution frame pairs, and proposed a transformer-based baseline method that outperformed state-of-the-art approaches.
Learning-based image deraining methods have made great progress. However, the lack of large-scale high-quality paired training samples is the main bottleneck to hamper the real image deraining (RID). To address this dilemma and advance RID, we construct a Large-scale High-quality Paired real rain benchmark (LHP-Rain), including 3000 video sequences with 1 million high-resolution (1920*1080) frame pairs. The advantages of the proposed dataset over the existing ones are three-fold: rain with higher-diversity and larger-scale, image with higher-resolution and higher-quality ground-truth. Specifically, the real rains in LHP-Rain not only contain the classical rain streak/veiling/occlusion in the sky, but also the \textbf{splashing on the ground} overlooked by deraining community. Moreover, we propose a novel robust low-rank tensor recovery model to generate the GT with better separating the static background from the dynamic rain. In addition, we design a simple transformer-based single image deraining baseline, which simultaneously utilize the self-attention and cross-layer attention within the image and rain layer with discriminative feature representation. Extensive experiments verify the superiority of the proposed dataset and deraining method over state-of-the-art.