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Image Super-Resolution Using Residual Convolutional Neural Network

In this paper, a single-image super-resolution using residual convolutional neural network (CNN) is presented. The proposed residual CNN model includes one short-cut and seven convolutional layers with the batch normalizations and ReLU activation functions. The transposed convolutional layer is also employed to eliminate the need of bicubic interpolation in the image preprocessing and to speed up the calculations of network. Several public benchmarks are tested to show that the proposed network architecture has better performance than the plain convolutional networks without using residual connection.