Image Super-Resolution Using Deep Convolutional Networks
Publication in refereed journal

替代計量分析
.

其它資訊
摘要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.
著者Dong C., Loy C.C., He K., Tang X.
期刊名稱IEEE Transactions on Pattern Analysis and Machine Intelligence
出版年份2016
月份2
日期1
卷號38
期次2
出版社Institute of Electrical and Electronics Engineers
出版地United States
頁次295 - 307
國際標準期刊號0162-8828
電子國際標準期刊號1939-3539
語言英式英語
關鍵詞deep convolutional neural networks, sparse coding, Super-resolution

上次更新時間 2020-09-08 於 03:48