Image denoising algorithm based on multi-channel GAN
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-...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-03-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021049/ |
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| _version_ | 1841539257425985536 |
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| author | Hongyan WANG Xiao YANG Yanchao JIANG Zumin WANG |
| author_facet | Hongyan WANG Xiao YANG Yanchao JIANG Zumin WANG |
| author_sort | Hongyan WANG |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-10b45a51e1b84c80b65f8b02841c4329 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-03-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-10b45a51e1b84c80b65f8b02841c43292025-01-14T07:21:54ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-03-014222923759741149Image denoising algorithm based on multi-channel GANHongyan WANGXiao YANGYanchao JIANGZumin WANGAiming 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021049/image denoisinggenerative adversarial networkchannel separationjoint perception loss |
| spellingShingle | Hongyan WANG Xiao YANG Yanchao JIANG Zumin WANG Image denoising algorithm based on multi-channel GAN Tongxin xuebao image denoising generative adversarial network channel separation joint perception loss |
| title | Image denoising algorithm based on multi-channel GAN |
| title_full | Image denoising algorithm based on multi-channel GAN |
| title_fullStr | Image denoising algorithm based on multi-channel GAN |
| title_full_unstemmed | Image denoising algorithm based on multi-channel GAN |
| title_short | Image denoising algorithm based on multi-channel GAN |
| title_sort | image denoising algorithm based on multi channel gan |
| topic | image denoising generative adversarial network channel separation joint perception loss |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021049/ |
| work_keys_str_mv | AT hongyanwang imagedenoisingalgorithmbasedonmultichannelgan AT xiaoyang imagedenoisingalgorithmbasedonmultichannelgan AT yanchaojiang imagedenoisingalgorithmbasedonmultichannelgan AT zuminwang imagedenoisingalgorithmbasedonmultichannelgan |