We propose a deep learning method for single image super-resolution (SR). Our
method directly learns an end-to-end mapping between the low/high-resolution
images. The mapping is represented as a deep convolutional neural network (CNN)
that takes the low-resolution image as the input and outputs the
high-resolution one. We further show that traditional sparse-coding-based SR
methods can also be viewed as a deep convolutional network. But unlike
traditional methods that handle each component separately, our method jointly
optimizes all layers. Our deep CNN has a lightweight structure, yet
demonstrates state-of-the-art restoration quality, and achieves fast speed for
practical on-line usage. We explore different network structures and parameter
settings to achieve trade-offs between performance and speed. Moreover, we
extend our network to cope with three color channels simultaneously, and show
better overall reconstruction quality.

本文提出一种深度学习方法用于图像超分辨率，采用卷积神经网络直接将低分辨率图片转换为高分辨率图片，能够实现较快的速度和超强的恢复质量，扩展支持三种颜色通道同时处理。