Semi-supervised learning for raindrop removal on a single image

Article type: Research Article

Authors: Guo, Wenbin | Zhang, Juan^{; *}

Affiliations: Shanghai University of Engineering and Technology

Correspondence: [*] Corresponding author. Juan Zhang, Shanghai University of Engineering and Technology. Tel.: +86 15821712631; Fax: +86 15821712631; E-mail: [email protected].

Abstract: This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised network and an unsupervised network. After the model is trained, the unsupervised network does not require paired images and it can get a clean image without raindrops. In particular, our network can perform training on paired and unpaired samples. The experimental results show that the best results are achieved not only on the supervised rain-removing network, but also on the unsupervised rain-removing network.

Keywords: Rain removal, raindrop detection, semi-supervised learning, image restoration, shared weight

DOI: 10.3233/JIFS-212342

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4041-4049, 2022