English
资源说明：图像超分辨 深度学习 cnn
Abstract. We propose a deep learning method for single image super-
resolution (SR). Our method directly learns an end-to-end mapping be-
tween the low/high-resolution images. The mapping is represented as
a deep convolutional neural network (CNN) [15] 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 meth-
ods 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.
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