Towards Unified Deep Image Deraining: A Survey and A New Benchmark
Xiang Chen Jinshan Pan Jiangxin Dong Jinhui Tang
Recent years have witnessed significant advances in image deraining due to
the kinds of effective image priors and deep learning models. As each deraining
approach has individual settings (e.g., training and test datasets, evaluation
criteria), how to fairly evaluate existing approaches comprehensively is not a
trivial task. Although existing surveys aim to review of image deraining
approaches comprehensively, few of them focus on providing unify evaluation
settings to examine the deraining capability and practicality evaluation. In
this paper, we provide a comprehensive review of existing image deraining
method and provide a unify evaluation setting to evaluate the performance of
image deraining methods. We construct a new high-quality benchmark named
HQ-RAIN to further conduct extensive evaluation, consisting of 5,000 paired
high-resolution synthetic images with higher harmony and realism. We also
discuss the existing challenges and highlight several future research
opportunities worth exploring. To facilitate the reproduction and tracking of
the latest deraining technologies for general users, we build an online
platform to provide the off-the-shelf toolkit, involving the large-scale
performance evaluation. This online platform and the proposed new benchmark are
publicly available and will be regularly updated at http://www.deraining.tech/.