Super-Resolution with Multi-Path Convolutional Neural Networks-Reference-Cited by-同舟云学术

Super-Resolution with Multi-Path Convolutional Neural Networks

Published:2020-06-01 Issue:6 Volume:140 Page:638-650
ISSN:0385-4221
Container-title:IEEJ Transactions on Electronics, Information and Systems
language:en
Short-container-title:IEEJ Trans.EIS

Author:

Urazoe Kazuya¹,Kuroki Nobutaka¹,Kato Yu¹,Ohtani Shinya¹,Hirose Tetsuya²,Numa Masahiro¹

Affiliation:

1. Graduate School of Engineering, Kobe University

2. Graduate School of Engineering, Osaka University

Publisher

Institute of Electrical Engineers of Japan (IEE Japan)

Subject

Electrical and Electronic Engineering

Link

https://www.jstage.jst.go.jp/article/ieejeiss/140/6/140_638/_pdf

Reference23 articles.

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2. (2) R. Timofte, V. D. Smet, and L. V. Gool : “A+ : Adjusted anchored neighborhood regression for fast super-resolution”, Asian Conference on Computer Vision (2014)

3. (3) J. Salvador and E. Perez-Pellitero : “Naive bayes super-resolution forest”, IEEE International Conference on Computer Vision (ICCV), pp. 325-333 (2015)

4. (4) E. P. Pellitero, J. Salvador, J. R. Hidalgo, and B. Rosenhahn : “PSyCo: Manifold span reduction for super resolution”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

5. (5) Y. Tian, F. Zhou, W. Yang, X. Shang, and Q. Liao : “Anchored neighborhood regression based single image super-resolution from self-examples”, IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, pp. 2827-2831 (2016)

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