Image Blind Denoising with Generative Adversarial Network Based Noise Modeling

In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images. As we all know, discriminative learning based methods, such as DnCNN, can achieve state-of-the-art denoising results, but they are not applicable to this problem due to the lack of...

Full description

Saved in:
Bibliographic Details
Published in2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 3155 - 3164
Main Authors Chen, Jingwen, Chen, Jiawei, Chao, Hongyang, Yang, Ming
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Adaptation models
Gallium nitride
Generative adversarial networks
Noise measurement
Noise reduction
Training
Training data
Online AccessGet full text

Cover

More Information
Summary:In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images. As we all know, discriminative learning based methods, such as DnCNN, can achieve state-of-the-art denoising results, but they are not applicable to this problem due to the lack of paired training data. To tackle the barrier, we propose a novel two-step framework. First, a Generative Adversarial Network (GAN) is trained to estimate the noise distribution over the input noisy images and to generate noise samples. Second, the noise patches sampled from the first step are utilized to construct a paired training dataset, which is used, in turn, to train a deep Convolutional Neural Network (CNN) for denoising. Extensive experiments have been done to demonstrate the superiority of our approach in image blind denoising.
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00333