EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration

Zeyu Xiao

Abstract:

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 crossscale similarities in the degraded image. Specifically,
we design two pyramid-based modules, i.e., the pyramid
progressive transfer (PPT) module and the pyramid selfattention (PSA) module, as the main components of the
proposed network. By taking several replicated blurry
images as inputs, the PPT module transfers both selfand 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). The implementation code is available at https:
//github.com/zeyuxiao1997/EDPN.