Example selective and order independent learning-based image super-resolution

Min Chen; Guoping Qiu; Kin Man Lam

Example selective and order independent learning-based image super-resolution

Min Chen, Guoping Qiu, Kin Man Lam

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

7 Citations (Scopus)

Abstract

In this paper, we present a novel example selective and order independent method for learning-based image super-resolution. We first present a method that selectively utilizes training samples according to the content of the input image. Experimental results show that by selecting the training samples appropriately, it is possible to dramatically reduce the computational costs without degrading image quality. We then present a new order independent technique that is shown to perform better than traditional order dependent techniques in learning image super-resolution and can also be applied to image editing such as region filling and object removal from images

Original language	English
Title of host publication	Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005
Pages	77-80
Number of pages	4
Volume	2005
Publication status	Published - 1 Dec 2005
Event	2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005 - Hong Kong, Hong Kong Duration: 13 Dec 2005 → 16 Dec 2005

Conference

Conference	2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005
Country/Territory	Hong Kong
City	Hong Kong
Period	13/12/05 → 16/12/05

ASJC Scopus subject areas

General Engineering

Cite this

@inproceedings{7c241d7a35bb41f8ac1abfdda6d0944f,

title = "Example selective and order independent learning-based image super-resolution",

abstract = "In this paper, we present a novel example selective and order independent method for learning-based image super-resolution. We first present a method that selectively utilizes training samples according to the content of the input image. Experimental results show that by selecting the training samples appropriately, it is possible to dramatically reduce the computational costs without degrading image quality. We then present a new order independent technique that is shown to perform better than traditional order dependent techniques in learning image super-resolution and can also be applied to image editing such as region filling and object removal from images",

author = "Min Chen and Guoping Qiu and Lam, {Kin Man}",

year = "2005",

month = dec,

day = "1",

language = "English",

isbn = "0780392663",

volume = "2005",

pages = "77--80",

booktitle = "Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005",

note = "2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005 ; Conference date: 13-12-2005 Through 16-12-2005",

}

Chen, M, Qiu, G & Lam, KM 2005, Example selective and order independent learning-based image super-resolution. in Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005. vol. 2005, 1595350, pp. 77-80, 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005, Hong Kong, Hong Kong, 13/12/05.

Example selective and order independent learning-based image super-resolution. / Chen, Min; Qiu, Guoping; Lam, Kin Man.
Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005. Vol. 2005 2005. p. 77-80 1595350.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Example selective and order independent learning-based image super-resolution

AU - Chen, Min

AU - Qiu, Guoping

AU - Lam, Kin Man

PY - 2005/12/1

Y1 - 2005/12/1

N2 - In this paper, we present a novel example selective and order independent method for learning-based image super-resolution. We first present a method that selectively utilizes training samples according to the content of the input image. Experimental results show that by selecting the training samples appropriately, it is possible to dramatically reduce the computational costs without degrading image quality. We then present a new order independent technique that is shown to perform better than traditional order dependent techniques in learning image super-resolution and can also be applied to image editing such as region filling and object removal from images

AB - In this paper, we present a novel example selective and order independent method for learning-based image super-resolution. We first present a method that selectively utilizes training samples according to the content of the input image. Experimental results show that by selecting the training samples appropriately, it is possible to dramatically reduce the computational costs without degrading image quality. We then present a new order independent technique that is shown to perform better than traditional order dependent techniques in learning image super-resolution and can also be applied to image editing such as region filling and object removal from images

UR - http://www.scopus.com/inward/record.url?scp=33847224524&partnerID=8YFLogxK

M3 - Conference article published in proceeding or book

SN - 0780392663

SN - 9780780392663

VL - 2005

SP - 77

EP - 80

BT - Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005

T2 - 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005

Y2 - 13 December 2005 through 16 December 2005

ER -

Example selective and order independent learning-based image super-resolution

Abstract

Conference

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this