A progressive approach for single image super-resolution

Convolutional neural network has achieved excellent success in single image super-resolution. In this paper, we present a progressive approach which reconstructs a high resolution image and optimizes the network at each level. In addition, our method can generate multi-scale HR image by one feed-forward network. The proposed method also utilizes the relationships among different scales, which help our network perform well on large scaling factors. Experiments on benchmark dataset demonstrate that our method achieves competitive performance against most state-of-the-art methods, especially for large scaling factors (e.g. 8×).