Jointly Solving Deblurring and Super-Resolution Problems with Dual Supervised Network

Zhenwen Liang, Dongyang Zhang, Jie Shao

Abstract: Thanks to the vigorous development of deep learning techniques, the solutions of super-resolution (SR) and deblurring have made great progress in recent years. However, in real-world scenarios, the low-quality images not only suffer from the loss of spatial information (resolution), they are often degradated by complicated non-uniform motion blur as well. As a result, traditional SR and deblurring methods are not robust enough to restore those blurred and low-resolution (LR) images to sharp and high-resolution (HR) images. Therefore, we propose a novel dual supervised network (DSN) to jointly solve the SR and deblurring problems. In the beginning we use a deblurring module to make blurry input much sharper and feed the deblurred feature to a reconstruction module, which aims to produce the final HR result from the deblurred feature. Moreover, we employ dual supervised structure to fully exploit the mutual dependencies between LR and HR patches. Experiments show that our network outperforms the state-of-the-art methods.