Single-image de-raining with feature-supervised generative adversarial network

Document Type

Article

Publication Date

5-1-2019

Abstract

De-raining, which aims at rain-steak removal from images, is a practical task in computer vision. However, it is difficult due to its ill-posed nature. In this letter, we propose a deep neural network architecture, feature-supervised generative adversarial network (FS-GAN) for single-image rain removal. Its main idea is to train a generative adversarial network (GAN) for which the supervision from ground truth is imposed on different layers of the generator network. We design a feature-supervised generator, a discriminator, an optimization target, as well as the detailed structure of FS-GAN. Experiments show that the proposed FS-GAN achieves better performance than state-of-the-art de-raining methods on both synthetic and real-world images in terms of quantitative and visual quality.

Identifier

85063594105 (Scopus)

Publication Title

IEEE Signal Processing Letters

External Full Text Location

https://doi.org/10.1109/LSP.2019.2903874

ISSN

10709908

First Page

650

Last Page

654

Issue

5

Volume

26

Grant

2016B010108010

Fund Ref

National Natural Science Foundation of China

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