Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87315
Title: Denoising adversarial networks for rain removal and reflection removal
Authors: Zheng, Qian
Shi, Boxin
Jiang, Xudong
Duan, Ling-Yu
Kot, Alex C.
Keywords: Engineering::Electrical and electronic engineering
Rain Streak Removal
Reflection Removal
Issue Date: 2019
Source: Zheng, Q., Shi, B., Jiang, X., Duan, L.-Y., & Kot, A. C. (2019). Denoising adversarial networks for rain removal and reflection removal. 2019 IEEE International Conference on Image Processing.
Abstract: This paper presents a novel adversarial scheme to perform im- age denoising for the tasks of rain streak removal and reflec- tion removal. Similar to several previous works, our denois- ing adversarial networks first estimate a prior image and then uses it to guide the inference of noise-free image. The novelty of our approach is to jointly learn the gradient and noise-free image based on an adversarial scheme. More specifically, we use the gradient map as the prior image. The inferred noise- free image guided by an estimated gradient is regarded as a negative sample, while the noise-free image guided by the ground truth of a gradient is taken as a positive sample. With the anchor defined by the ground truth of noise-free image, we play a min-max game to jointly train two optimizers for the estimation of the gradient and the inference of noise-free images. We show that both prior image and noise-free image can be accurately obtained under this adversarial scheme. Our state-of-the-art performances achieved on two public bench- mark datasets validate the effectiveness of our approach.
URI: https://hdl.handle.net/10356/87315
http://hdl.handle.net/10220/49451
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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