99 research outputs found
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal
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.Comment: Accepted by ICCV 202
Toward Real-world Single Image Deraining: A New Benchmark and Beyond
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 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-1
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