  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.
