Abstract
Recently, the performance of single image super-resolution (SISR) methods have been significantly improved with the development of the convolutional neural networks (CNN). In this paper, we propose a very deep dense convolutional network (SRDCN) for image super-resolution. Due to the dense connection, the feature maps of each preceding layer are connected and used as inputs of all subsequent layers, thus utilizing both low-level and high-level features. In addition, residual learning and dense skip connection are adopted to ease the difficulties of training very deep convolutional networks by alleviating the vanishing-gradient problem. Experimental results on four benchmark datasets demonstrate that our proposed method achieves comparable performance with other state-of-the-art methods.
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