Abstract
With the recent development of deep learning, super-resolution of a single image has been verified to improve the accuracy. These methods are implemented in a fully supervised manner for super-resolving the observed LR images with the known degradation model, and thus it faces difficulty to recover high-resolution images from the low-resolution images captured under unknown degradation models. In this study, we propose a deep unsupervised learning network to solve these problems. The proposed network architecture consists of a generative network for predicting high-resolution images and a degradation module for automatically learning the degradation operations for observed low-resolution images. Specifically, we exploit a Encoder-Decoder structure to serve as the generative network, which has been proven to have the powerful capability for modeling high quality images while the degradation module is implemented with a special depth-wise convolution layer, where its parameters are learnable. Therefore the proposed unsupervised learning SR framework is implemented in an end-to-end learning network training of the degradation module. To verify the effectiveness of the proposed method, we conduct extensive experiments on three publicly available benchmark datasets, and manifest superior performance even for the LR images captured under complex degradation models.
