Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image
Abstract
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.
Keywords
Cite
@article{arxiv.1909.11983,
title = {Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image},
author = {Qingbo Wu and Lei Wang and King N. Ngan and Hongliang Li and Fanman Meng and Linfeng Xu},
journal= {arXiv preprint arXiv:1909.11983},
year = {2019}
}
Comments
In this revision, we add the comparison with our previous exploration towards the de-raining quality assessment in Ref. [16]. Some typos in Tables III and IV are corrected, where the missed minus signs are added back for some OU metrics