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Learning-based image deraining methods have made great progress. However, thelack of large-scale high-quality paired training samples is the main bottleneckto hamper the real image deraining (RID). To address this dilemma and advanceRID, 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 theexisting ones are three-fold: rain with higher-diversity and larger-scale,image with higher-resolution and higher-quality ground-truth. Specifically, thereal rains in LHP-Rain not only contain the classical rainstreak/veiling/occlusion in the sky, but also the \textbf{splashing on theground} overlooked by deraining community. Moreover, we propose a novel robustlow-rank tensor recovery model to generate the GT with better separating thestatic background from the dynamic rain. In addition, we design a simpletransformer-based single image deraining baseline, which simultaneously utilizethe self-attention and cross-layer attention within the image and rain layerwith discriminative feature representation. Extensive experiments verify thesuperiority of the proposed dataset and deraining method over state-of-the-art.

From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal