Learning a Cascaded Non-Local Residual Network for Super-Resolving Blurry Images
Deblurring low-resolution images is quite challenging as blur exists in the images and the resolution of the images is low. Existing deblurring methods usually require high-resolution input while the super-resolution methods usually assume that the blur is known or small. Simply applying the deblurring and super-resolution does not solve this problem well. In this paper, we develop an effective cascaded non-local residual network which cascades the deblurring module and super-resolution module to estimate latent high-resolution images from blurry low-resolution ones. The network first uses the deblurring module to generate intermediate clear features and then develops a non-local residual network (NLRN) as the super-resolution module to generate clear high-resolution images from the intermediate clear features. To better constrain the network and reduce the training difficulty, we develop an effective constraint based on image gradients for edge preservation and adopt the progressive upsampling mechanism. We train the proposed network in an end-to-end manner. Both quantitative and qualitative results on the benchmarks demonstrate the effectiveness of the proposed method. Moreover, the proposed method achieves top-3 performance on the low-resolution track of the NTIRE 2021 Image Deblurring Challenge.