Recent research on super-resolution has progressed with the development of
deep convolutional neural networks (DCNN). In particular, residual learning
techniques exhibit improved performance. In this paper, we develop an enhanced
deep super-resolution network (EDSR) with performance exceeding those of
current state-of-the-art SR methods. The significant performance improvement of
our model is due to optimization by removing unnecessary modules in
conventional residual networks. The performance is further improved by
expanding the model size while we stabilize the training procedure. We also
propose a new multi-scale deep super-resolution system (MDSR) and training
method, which can reconstruct high-resolution images of different upscaling
factors in a single model. The proposed methods show superior performance over
the state-of-the-art methods on benchmark datasets and prove its excellence by
winning the NTIRE2017 Super-Resolution Challenge.

本文介绍提出的增强型深度超分辨网络（EDSR）和多尺度深度超分辨系统（MDSR），通过深度卷积神经网络（DCNN）和残差学习等技术，去除不必要的模块，并扩展模型大小和稳定的训练过程，实现比当前现有的超分辨率方法更高的性能和不同放大因子的高分辨率图像重构，表现卓越，赢得 NTIRE2017 超分辨挑战。