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Specifically, residual learning and batch\nnormalization are utilized to speed up the training process as well as boost\nthe denoising performance. Different from the existing discriminative denoising\nmodels which usually train a specific model for additive white Gaussian noise\n(AWGN) at a certain noise level, our DnCNN model is able to handle Gaussian\ndenoising with unknown noise level (i.e., blind Gaussian denoising). With the\nresidual learning strategy, DnCNN implicitly removes the latent clean image in\nthe hidden layers. This property motivates us to train a single DnCNN model to\ntackle with several general image denoising tasks such as Gaussian denoising,\nsingle image super-resolution and JPEG image deblocking. Our extensive\nexperiments demonstrate that our DnCNN model can not only exhibit high\neffectiveness in several general image denoising tasks, but also be efficiently\nimplemented by benefiting from GPU computing.","url_abs":"http://arxiv.org/abs/1608.03981v1","url_pdf":"http://arxiv.org/pdf/1608.03981v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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