  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
(AWGN) 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.
