Cross View Capture for Stereo Image Super-Resolution.pdf (9.14 MB)
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Cross view capture for stereo image super-resolution

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journal contribution
posted on 15.07.2021, 12:38 authored by Xiangyuan Zhu, Kehua Guo, Hui FangHui Fang, Liang Chen, Sheng Ren, Bin Hu

Stereo image super-resolution exploits additional features from cross view image pairs for high resolution (HR) image reconstruction. Recently, several new methods have been proposed to investigate cross view features along epipolar lines to enhance the visual perception of recovered HR images. Despite the impressive performance of these methods, global contextual features from cross view images are left unexplored. In this paper, we propose a cross view capture network (CVCnet) for stereo image super-resolution by using both global contextual and local features extracted from both views. Specifically, we design a cross view block to capture diverse feature embeddings from the views in stereo vision. In addition, a cascaded spatial perception module is proposed to redistribute each location in feature maps according to the weight it occupies to make the extraction of features more effective. Extensive experiments demonstrate that our proposed CVCnet outperforms the state-of-the-art image super-resolution methods to achieve the best performance for stereo image super-resolution tasks. The source code is available at https://github.com/xyzhu1/CVCnet.

Funding

Natural Science Foundation of China under Grant 62076255

Hunan Provincial Science and Technology Plan Project 2020SK2059

National Science Foundation of Hunan Province, China, under Grant 2019JJ20025 and Grant 2019JJ40406

National Social Science Fund of China (No. 20&ZD120)

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Multimedia

Volume

24

Pages

3074 - 3086

Publisher

Institute of Electrical and Electronics Engineers

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

23/06/2021

Publication date

2021-06-25

Copyright date

2021

ISSN

1520-9210

eISSN

1941-0077

Language

en

Depositor

Dr Hui Fang. Deposit date: 23 June 2021

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