基于多通道GAN的图像去噪算法

王洪雁; 杨晓; 姜艳超; 汪祖民

doi:10.11959/j.issn.1000-436x.2021049

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基于多通道GAN的图像去噪算法

学术通信 | 更新时间：2024-06-05

- 基于多通道GAN的图像去噪算法
- Image denoising algorithm based on multi-channel GAN
- 通信学报 2021年42卷第3期页码：229-237
- 作者机构：
  
  1. 浙江理工大学信息学院，浙江杭州 310018
  2. 大连大学信息工程学院，辽宁大连 116622
  3. 五邑大学智能制造学部，广东江门 529020
- 作者简介：
  
  [ "王洪雁（1979– ），男，河南南阳人，博士，浙江理工大学特聘教授、硕士生导师，主要研究方向为稀疏学习、阵列信号处理、参数估计、机器视觉等。" ]
  [ "杨晓（1997– ），女，山东德州人，大连大学硕士生，主要研究方向为图像处理、机器视觉等。" ]
  [ "姜艳超（1985– ），女，河北唐山人，博士，大连大学硕士生导师，主要研究方向为信号处理、机器学习等。" ]
  [ "汪祖民（1975– ），男，河南信阳人，博士，大连大学教授、硕士生导师，主要研究方向为信号处理、机器学习等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61301258);国家自然科学基金资助项目(61871164);浙江省自然科学基金重点项目(LZ21F010002);中国博士后科学基金资助项目(2016M590218)
- DOI：10.11959/j.issn.1000-436x.2021049
  中图分类号： TP391
- 网络出版日期：2021-03，
  
  纸质出版日期：2021-03-25
- 稿件说明：
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王洪雁, 杨晓, 姜艳超, 等. 基于多通道GAN的图像去噪算法[J]. 通信学报, 2021,42(3):229-237.

Hongyan WANG, Xiao YANG, Yanchao JIANG, et al. Image denoising algorithm based on multi-channel GAN[J]. Journal on communications, 2021, 42(3): 229-237.
王洪雁, 杨晓, 姜艳超, 等. 基于多通道GAN的图像去噪算法[J]. 通信学报, 2021,42(3):229-237. DOI： 10.11959/j.issn.1000-436x.2021049.

Hongyan WANG, Xiao YANG, Yanchao JIANG, et al. Image denoising algorithm based on multi-channel GAN[J]. Journal on communications, 2021, 42(3): 229-237. DOI： 10.11959/j.issn.1000-436x.2021049.

摘要

针对图像采集和传输过程中所产生噪声导致后续图像处理能力下降的问题，提出基于生成对抗网络（GAN）的多通道图像去噪算法。所提算法将含噪彩色图像分离为RGB三通道，各通道基于具有相同架构的端到端可训练的GAN实现去噪。GAN生成网络基于U-net衍生网络以及残差块构建，从而可参考低级特征信息以有效提取深度特征进而避免丢失细节信息；判别网络则基于全卷积网络构造，因而可获得像素级分类从而提升判别精确性。此外，为改善去噪能力且尽可能保留图像细节信息，所构建去噪网络基于对抗损失、视觉感知损失和均方误差损失这3类损失度量构建复合损失函数。最后，利用算术平均方法融合三通道输出信息以获得最终去噪图像。实验结果表明，与主流算法相比，所提算法可有效去除图像噪声，且可较好地恢复原始图像细节。

Abstract

Aiming at the issue that the noise generated during image acquisition and transmission would degrade the ability of subsequent image processing

a generative adversarial network (GAN) based multi-channel image denoising algorithm was developed.The noisy color image could be separated into red-green-blue (RGB) three channels via the proposed approach

and then the denoising could be implemented in each channel on the basis of an end-to-end trainable GAN with the same architecture.The generator module of GAN was constructed based on the U-net derivative network and residual blocks such that the high-level feature information could be extracted effectively via referring to the low-level feature information to avoid the loss of the detail information.In the meantime

the discriminator module could be demonstrated on the basis of fully convolutional neural network such that the pixel-level classification could be achieved to improve the discrimination accuracy.Besides

in order to improve the denoising ability and retain the image detail as much as possible

the composite loss function could be depicted by the illustrated denoising network based on the following three loss measures

adversarial loss

visual perception loss

and mean square error (MSE).Finally

the resultant three-channel output information could be fused by exploiting the arithmetic mean method to obtain the final denoised image.Compared with the state-of-the-art algorithms

experimental results show that the proposed algorithm can remove the image noise effectively and restore the original image details considerably.

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Keywords

references

KUNSOTH R , BISWAS M . Modified decision based median filter for impulse noise removal [C ] // 2016 International Conference on Wireless Communications,Signal Processing and Networking . Piscataway:IEEE Press , 2016 : 1316 - 1319 .

TUKEY J W . Exploratory data analysis [J ] . Journal of the American Statistical Association , 1977 , 28 ( 1 ): 163 - 182 .

SUMAN S . Image denoising using new adaptive based median filter [J ] . Signal ＆ Image Processing:An International Journal , 2014 , 5 ( 4 ): 1 - 13 .

HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition [C ] // IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 770 - 778 .

AHARON M , ELAD M , BRUCKSTEIN A . K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation [J ] . IEEE Transactions on Signal Processing , 2006 , 54 ( 11 ): 4311 - 4322 .

DABOV K , FOI A , KATKOVNIK V , et al . Image denoising by sparse 3-D transform-domain collaborative filtering [J ] . IEEE Transactions on Image Processing , 2007 , 16 ( 8 ): 2080 - 2095 .

JAIN V , SEUNG H S . Natural image denoising with convolutional networks [C ] // International Conference on Neural Information Processing Systems . Piscataway:IEEE Press , 2008 : 769 - 776 .

ZHANG K , ZUO W M , CHEN Y J , et al . Beyond a Gaussian denoiser:residual learning of deep CNN for image denoising [J ] . IEEE transactions on Image Processing , 2017 , 26 ( 7 ): 3142 - 3155 .

RONNEBERGER O , FISCHER P , BROX T . U-net:convolutional networks for biomedical image segmentation [C ] // International Conference on Medical Image Computing and Computer-Assisted Intervention . Berlin:Springer , 2015 : 234 - 241 .

HEINRICH M P , STILLE M , BUZUG T M , et al . Residual U-net convolutional neural network architecture for low-dose CT denoising [J ] . Current Directions in Biomedical Engineering , 2018 , 4 ( 1 ): 297 - 300 .

MAO S R , JIN R H , YANG C , et al . Denoising of 3D magnetic resonance images using a residual encoder-decoder Wasserstein generative adversarial network [J ] . Medical Image Analysis , 2019 , 55 ( 7 ): 165 - 180 .

DIVAKAR N , BABU R V . Image denoising via CNNs:an adversarial approach [C ] // Computer Vision and Pattern Recognition Workshops . Piscataway:IEEE Press , 2017 : 1076 - 1083 .

WOLTERINK J M , LEINER T , VIERGEVER M A , et al . Generative adversarial networks for noise reduction in low-dose CT [J ] . IEEE Transactions on Medical Imaging , 2017 , 36 ( 12 ): 2536 - 2545 .

YANG Q , YAN P , ZHANG Y , et al . Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss [J ] . IEEE Transactions on Medical Imaging , 2018 , 37 ( 6 ): 1348 - 1357 .

PARK H S , BAEK J , YOU S K , et al . Unpaired image denoising using a generative adversarial network in X-ray CT [J ] . IEEE Access , 2019 , 7 : 10414 - 10425 .

RAJEEV R , SAMATH J A , KARTHIKEYAN N K . An intelligent recurrent neural network with long short-term memory (LSTM) BASED batch normalization for medical image denoising [J ] . Journal of Medical Systems , 2019 , 43 ( 8 ): 1 - 10 .

TRIPATHI P C , BAG S . CNN-DMRI:a convolutional neural network for denoising of magnetic resonance images [J ] . Pattern Recognition Letters , 2020 , 135 : 57 - 63 .

SHIN S Y , KIM D M , SUH J W . Image denoiser using convolutional neural network with deconvolution and modified residual network [J ] . ICE Transactions on Information and Systems , 2019 , 102 ( 8 ): 1598 - 1601 .

CHEN S , SHI D , SADIQ M , et al . Image denoising with generative adversarial networks and its application to cell image enhancement [J ] . IEEE Access , 2020 , 8 : 82819 - 82831 .

JIA Y M , WEI Y , PENG W L , et al . FusionGAN:a generative adversarial network for infrared and visible image fusion [J ] . Information Fusion , 2019 , 48 : 11 - 26 .

UDDIN A F , CHUNG T C , BAE S H . A perceptually inspired new blind image denoising method using L1 and perceptual loss [J ] . IEEE Access , 2019 , 7 : 90538 - 90549 .

史再峰 , 李金卓 , 曹清洁 , 等 . 基于生成对抗网络的低剂量能谱层析成像去噪算法 [J ] . 吉林大学学报(工学版) , 2020 , 50 ( 5 ): 1755 - 1764 .

SHI Z F , LI J Z , CAO Q J , et al . Low-dose spectral CT denoising method via a generative adversarial network [J ] . Journal of Jilin University (Engineering and Technology Edition) , 2020 , 50 ( 5 ): 1755 - 1764 .

JI H , LIU C , SHEN Z , et al . Robust video denoising using low rank matrix completion [C ] // 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2010 : 1791 - 1798 .

MA K , ZENG K , WANG Z . Perceptual quality assessment for multi-exposure image fusion [J ] . IEEE Transactions on Image Processing a Publication of the IEEE Signal Processing Society , 2015 , 24 ( 11 ): 3345 - 3356 .

WANG X , YU K , WU S , et al . ESRGAN:enhanced super-resolution generative adversarial networks [C ] // Computer Vision ECCV 2018 Workshops . Berlin:Springer , 2018 : 63 - 79 .

LEDIG C , THEIS L , HUSZAR F , et al . Photo-realistic single image super-resolution using a generative adversarial network [C ] // 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2017 : 105 - 114 .

RUI H W , XIONG W X , BING X G , et al . An effective image denoising method for UAV images via improved generative adversarial networks [J ] . Sensors , 2018 , 18 ( 7 ): 1985 .

CHEN Z , ZENG Z , SHEN H , et al . DN-GAN:denoising generative adversarial networks for speckle noise reduction in optical coherence tomography images [J ] . Biomedical Signal Processing and Control , 2020 , 55 : 1 - 10 .

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