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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...
In this paper we are interested in exploiting self-similarity information for discriminative image denoising. Towards this goal, we propose a simple yet powerful denoising method based on transductive Gaussian processes, which introduces self-similarity in the prediction stage. Our approach allows to build a rich similarity measure by learning hyper parameters defining multi-kernel combinations. We...
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