Encoder-Decoder Residual Network for Real Super-Resolution

Guoan Cheng, Ai Matsune, Qiuyu Li, Leilei Zhu, Huaijuan Zang, Shu Zhan

Abstract: Real single image super-resolution is a challenging task to restore lost information and attenuate noise from images mixed unknown degradations complicatedly. Classic single image super-resolution, aims to enhance the resolution of bicubically degraded images, has recently obtained great success via deep learning. However, these existing methods do not perform well for real single image super-resolution. In this paper, we propose an Encoder-Decoder Residual Network (EDRN) for real single image super-resolution. We adopt an encoder-decoder structure to encode highly effective features and embed the coarse-to-fine method. The coarse-to-fine structure can gradually restore lost information and reduce noise effects. We empirically rethink and discuss the usage of batch normalization. Compared with state-of-the-art methods in classic single image super-resolution, our EDRN can efficiently restore the corresponding high-resolution image from a degraded input image. Our EDRN achieved the 9th place for PSNR and top 5 for SSIM in the final result of NTIRE 2019 Real Super-resolution Challenge. The source code and the trained model are available at https://github.com/yyknight/NTIRE2019_EDRN.