Accurate Image Super-Resolution Using Very Deep Convolutional Networks

We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19]. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers....

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Bibliographic Details
Published in:	2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1646 - 1654
Main Authors:	Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2016
Subjects:	Convergence Convolution Image reconstruction Signal resolution Spatial resolution Training
ISSN:	1063-6919
Online Access:	Get full text
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Summary:	We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19]. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates (104 times higher than SRCNN [6]) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.
ISSN:	1063-6919
DOI:	10.1109/CVPR.2016.182