Accelerating the Super-Resolution Convolutional Neural Network
Refereed conference paper presented and published in conference proceedings


引用次數
替代計量分析
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其它資訊
摘要As a successful deep model applied in image super-resolution (SR),
the Super-Resolution Convolutional Neural Network (SRCNN) [1, 2] has demonstrated
superior performance to the previous hand-crafted models either in speed
and restoration quality. However, the high computational cost still hinders it from
practical usage that demands real-time performance (24 fps). In this paper, we aim
at accelerating the current SRCNN, and propose a compact hourglass-shape CNN
structure for faster and better SR. We re-design the SRCNN structure mainly in
three aspects. First, we introduce a deconvolution layer at the end of the network,
then the mapping is learned directly from the original low-resolution image
(without interpolation) to the high-resolution one. Second, we reformulate
the mapping layer by shrinking the input feature dimension before mapping and
expanding back afterwards. Third, we adopt smaller filter sizes but more mapping
layers. The proposed model achieves a speed up of more than 40 times with
even superior restoration quality. Further, we present the parameter settings that
can achieve real-time performance on a generic CPU while still maintaining good
performance. A corresponding transfer strategy is also proposed for fast training
and testing across different upscaling factors.
著者Chao Dong, Chen Change Loy, Xiaoou Tang
會議名稱The 14th European Conference on Computer Vision
會議開始日08.10.2016
會議完結日16.10.2016
會議地點Amsterdam
會議國家/地區荷蘭
會議論文集題名Computer Vision – ECCV 2016
出版年份2016
月份10
卷號9906
出版社Springer
國際標準書號978-3-319-46474-9
國際標準期刊號0302-9743
語言美式英語

上次更新時間 2020-07-08 於 02:15