Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/485553
Title: Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Authors: Kai Zhang;Wangmeng Zuo;Yunjin Chen;Deyu Meng;Lei Zhang
Year: 2017
Publisher: IEEE
Abstract: The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.
URI: http://dl.kums.ac.ir/handle/Hannan/485553
volume: 26
issue: 7
More Information: 3142,
3155
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
7839189.pdf5.83 MBAdobe PDFThumbnail
Preview File
Title: Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Authors: Kai Zhang;Wangmeng Zuo;Yunjin Chen;Deyu Meng;Lei Zhang
Year: 2017
Publisher: IEEE
Abstract: The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.
URI: http://dl.kums.ac.ir/handle/Hannan/485553
volume: 26
issue: 7
More Information: 3142,
3155
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
7839189.pdf5.83 MBAdobe PDFThumbnail
Preview File
Title: Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Authors: Kai Zhang;Wangmeng Zuo;Yunjin Chen;Deyu Meng;Lei Zhang
Year: 2017
Publisher: IEEE
Abstract: The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.
URI: http://dl.kums.ac.ir/handle/Hannan/485553
volume: 26
issue: 7
More Information: 3142,
3155
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
7839189.pdf5.83 MBAdobe PDFThumbnail
Preview File