Abstract
Single image super-resolution via convolutional neural network (CNN) has demonstrated superior performance. In this paper, we propose a deep CNN model named super-resolution dense residual convolutional network (SRDCR) with the goal of reconstructing high quality high-resolution (HR) image. We propose a dense residual block (DRB) to learn residual information by residual connected layers. The local fusion layer (LFL) is then used to adaptively fuse the input of DRB and the output of the last residual layer. After multiple DRBs residual learning, the global fusion layer (GFL) reconstructs an HR image by adaptively combining the original low-resolution (LR) information and learned information. Experiments on extensive benchmark show that our method achieves favorable performance with much less CNN layers than DRB network.
Published Version
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