Image Super Resolution Using Deep Convolutional Networks
We propose a deep learning method for single image super resolution (SR) Our method directly learns an end to end mapping between the low/high resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low resolution image as the input and outputs the high resolution one. We further show that traditional sparse coding based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state of the art restoration quality, and achieves fast speed for practical on line usage. We explore different network structures and parameter settings to achieve trade offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.
IEEE Transactions on Pattern Analysis and Machine Intelligence