research-article

Remote Sensing Image Super-resolution Using Dual-Dictionary Pairs Based on Sparse Presentation and Multiple Features

Authors:

Binyu YanAuthors Info & Claims

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

Pages 90 - 94

https://doi.org/10.1145/2632856.2632921

Published: 10 July 2014 Publication History

Abstract

In this paper, a super-resolution method based on sparse dictionary and multiple futures is proposed for remote sensing images. Super-resolution aims to reconstruct the high-frequency detail from the low resolution image. In this paper, high frequency is decomposed into two parts: primary high-frequency and residual high frequency. We proposed dual-dictionary pairs, i.e. primitive sparse dictionary pair and residual sparse dictionary pair to recover primary high-frequency and residual high frequency respectively. To describe the image more precise, we use multiple features to describe the structure of the image, and combine them together to present the image. Then use the combination futures to train the dictionary. The experimental results show that the proposed algorithm has a good performance, and the high-resolution image generated by the proposed method is with better subjective and objective quality compared with other methods.

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Cited By

Xinlei WNaifeng L(2018)Super-resolution of remote sensing images via sparse structural manifold embeddingNeurocomputing10.1016/j.neucom.2015.09.012173:P3(1402-1411)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1016/j.neucom.2015.09.012
Wu WYang XLiu KLiu YYan Bhua H(2016)A new framework for remote sensing image super-resolutionJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2015.11.00564:C(63-75)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1016/j.sysarc.2015.11.005
Yang XWu WHua HLiu K(2015)Infrared Image Recovery from Visible Image by Using Multi-scale and Multi-view Sparse RepresentationProceedings of the 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2015.103(554-559)Online publication date: 23-Nov-2015
https://dl.acm.org/doi/10.1109/SITIS.2015.103

Index Terms

Remote Sensing Image Super-resolution Using Dual-Dictionary Pairs Based on Sparse Presentation and Multiple Features
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation

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cover image ACM Other conferences

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

July 2014

430 pages

ISBN:9781450328104

DOI:10.1145/2632856

General Chairs:
Hanzi Wang
Xiamen University, China
,
Larry Davis
University of Maryland, USA
,
Program Chairs:
Wenwu Zhu
Tsinghua University, China
,
Stephan Kopf
University of Mannheim, Germany
,
Yanyun Qu
Xiamen University, China
,
Publications Chairs:
Jun Yu
Xiamen University, China
,
Jitao Sang
Institute of Automation, CAS, China
,
Tao Mei
Microsoft Research Asia, China

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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NSF of China: National Natural Science Foundation of China
Beijing ACM SIGMM Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2014

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ICIMCS '14

ICIMCS '14: International Conference on Internet Multimedia Computing and Service

July 10 - 12, 2014

Xiamen, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

Xinlei WNaifeng L(2018)Super-resolution of remote sensing images via sparse structural manifold embeddingNeurocomputing10.1016/j.neucom.2015.09.012173:P3(1402-1411)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1016/j.neucom.2015.09.012
Wu WYang XLiu KLiu YYan Bhua H(2016)A new framework for remote sensing image super-resolutionJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2015.11.00564:C(63-75)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1016/j.sysarc.2015.11.005
Yang XWu WHua HLiu K(2015)Infrared Image Recovery from Visible Image by Using Multi-scale and Multi-view Sparse RepresentationProceedings of the 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2015.103(554-559)Online publication date: 23-Nov-2015
https://dl.acm.org/doi/10.1109/SITIS.2015.103

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