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Proceedings Paper

Deep CNN jointing low-high level feature for image super-resolution
Author(s): Xuhui Song; Weirong Liu; Jie Liu; Chaorong Liu; Chunyan Lu; Huiling Gao
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Paper Abstract

Image super-resolution methods based on forward-feed convolutional neural networks (CNN) reconstruct the image with more details and sharper texture. However, most of these methods do not consider the influence of high level semantic feature to improve image perceptual effect. In this paper, we propose a deep CNN architecture jointing low-high level feature for image super-resolution. Our method uses 17 weight layers to predict residual between the high resolution and low resolution image. And we joint the low level and high level image features to constraint the network parameters updating. Experimental results validate that our method reconstruct the high resolution images with clear edge and less warp.

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110693O (6 May 2019); doi: 10.1117/12.2524412
Show Author Affiliations
Xuhui Song, Lanzhou Univ. of Technology (China)
Weirong Liu, Lanzhou Univ. of Technology (China)
Jie Liu, Lanzhou Univ. of Technology (China)
Chaorong Liu, Lanzhou Univ. of Technology (China)
Chunyan Lu, Lanzhou Univ. of Technology (China)
Huiling Gao, Lanzhou Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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