  Image deblurring has seen a great improvement with the development of deep
neural networks. In practice, however, blurry images often suffer from
additional degradations such as downscaling and compression. To address these
challenges, we propose an Enhanced Deep Pyramid Network (EDPN) for blurry image
restoration from multiple degradations, by fully exploiting the self- and
cross-scale similarities in the degraded image.Specifically, we design two
pyramid-based modules, i.e., the pyramid progressive transfer (PPT) module and
the pyramid self-attention (PSA) module, as the main components of the proposed
network. By taking several replicated blurry images as inputs, the PPT module
transfers both self- and cross-scale similarity information from the same
degraded image in a progressive manner. Then, the PSA module fuses the above
transferred features for subsequent restoration using self- and
spatial-attention mechanisms. Experimental results demonstrate that our method
significantly outperforms existing solutions for blurry image super-resolution
and blurry image deblocking. In the NTIRE 2021 Image Deblurring Challenge, EDPN
achieves the best PSNR/SSIM/LPIPS scores in Track 1 (Low Resolution) and the
best SSIM/LPIPS scores in Track 2 (JPEG Artifacts).
