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. Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
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