  Images acquired by outdoor vision systems easily suffer poor visibility and
annoying interference due to the rainy weather, which brings great challenge
for accurately understanding and describing the visual contents. Recent
researches have devoted great efforts on the task of rain removal for improving
the image visibility. However, there is very few exploration about the quality
assessment of de-rained image, even it is crucial for accurately measuring the
performance of various de-raining algorithms. In this paper, we first create a
de-raining quality assessment (DQA) database that collects 206 authentic rain
images and their de-rained versions produced by 6 representative single image
rain removal algorithms. Then, a subjective study is conducted on our DQA
database, which collects the subject-rated scores of all de-rained images. To
quantitatively measure the quality of de-rained image with non-uniform
artifacts, we propose a bi-directional feature embedding network (B-FEN) which
integrates the features of global perception and local difference together.
Experiments confirm that the proposed method significantly outperforms many
existing universal blind image quality assessment models. To help the research
towards perceptually preferred de-raining algorithm, we will publicly release
our DQA database and B-FEN source code on https://github.com/wqb-uestc.
