Detection methods of salient regions in super-resolution based on sparse representation

Omer Berk; Gulcihan Ozdemir; Behcet Ugur Toreyin; Abdulkerim Capar

doi:10.1109/TIPTEKNO.2019.8895204

Detection methods of salient regions in super-resolution based on sparse representation

Omer Berk, Gulcihan Ozdemir, Behcet Ugur Toreyin, Abdulkerim Capar

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Achieving successful results with the sparse representation in super-resolution increases the interest in the field. The sparse representation model, which is an important method in super-resolution, consists of image patches, a correct dictionary and a sparse linear combination of the elements of this dictionary. At this point, the super-resolution successfully reflects the sparse pattern by obtaining high-resolution images with the sparse pattern from low-resolution image patches. The detection of image regions is critical here. In the proposed method, the successes of the results are compared by using Fuzzy C-Means Clustering and Hue-Saturation-Value (HSV) Based Segmentation methods for determination of these regions.

Translated title of the contribution	Seyrek gösterime dayali süper-çözünürlükte belirgin bölgelerin tespit metotlari
Original language	English
Title of host publication	TIPTEKNO 2019 - Tip Teknolojileri Kongresi
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728124209
DOIs	https://doi.org/10.1109/TIPTEKNO.2019.8895204
Publication status	Published - Oct 2019
Event	2019 Medical Technologies Congress, TIPTEKNO 2019 - Izmir, Turkey Duration: 3 Oct 2019 → 5 Oct 2019

Publication series

Name	TIPTEKNO 2019 - Tip Teknolojileri Kongresi

Conference

Conference	2019 Medical Technologies Congress, TIPTEKNO 2019
Country/Territory	Turkey
City	Izmir
Period	3/10/19 → 5/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Dictionary
Fuzzy c-means clustering
Hsv segmentation
Image processing
Sparse representation
Superresolution

Access to Document

10.1109/TIPTEKNO.2019.8895204

Cite this

@inproceedings{c2de45b0f62f477caf2cb3e5da8b6dbd,

title = "Detection methods of salient regions in super-resolution based on sparse representation",

abstract = "Achieving successful results with the sparse representation in super-resolution increases the interest in the field. The sparse representation model, which is an important method in super-resolution, consists of image patches, a correct dictionary and a sparse linear combination of the elements of this dictionary. At this point, the super-resolution successfully reflects the sparse pattern by obtaining high-resolution images with the sparse pattern from low-resolution image patches. The detection of image regions is critical here. In the proposed method, the successes of the results are compared by using Fuzzy C-Means Clustering and Hue-Saturation-Value (HSV) Based Segmentation methods for determination of these regions.",

keywords = "Dictionary, Fuzzy c-means clustering, Hsv segmentation, Image processing, Sparse representation, Superresolution",

author = "Omer Berk and Gulcihan Ozdemir and Toreyin, {Behcet Ugur} and Abdulkerim Capar",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Medical Technologies Congress, TIPTEKNO 2019 ; Conference date: 03-10-2019 Through 05-10-2019",

year = "2019",

month = oct,

doi = "10.1109/TIPTEKNO.2019.8895204",

language = "English",

series = "TIPTEKNO 2019 - Tip Teknolojileri Kongresi",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "TIPTEKNO 2019 - Tip Teknolojileri Kongresi",

address = "United States",

}

Berk, O, Ozdemir, G , Toreyin, BU & Capar, A 2019, Detection methods of salient regions in super-resolution based on sparse representation. in TIPTEKNO 2019 - Tip Teknolojileri Kongresi., 8895204, TIPTEKNO 2019 - Tip Teknolojileri Kongresi, Institute of Electrical and Electronics Engineers Inc., 2019 Medical Technologies Congress, TIPTEKNO 2019, Izmir, Turkey, 3/10/19. https://doi.org/10.1109/TIPTEKNO.2019.8895204

Detection methods of salient regions in super-resolution based on sparse representation. / Berk, Omer; Ozdemir, Gulcihan ; Toreyin, Behcet Ugur et al.
TIPTEKNO 2019 - Tip Teknolojileri Kongresi. Institute of Electrical and Electronics Engineers Inc., 2019. 8895204 (TIPTEKNO 2019 - Tip Teknolojileri Kongresi).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detection methods of salient regions in super-resolution based on sparse representation

AU - Berk, Omer

AU - Ozdemir, Gulcihan

AU - Toreyin, Behcet Ugur

AU - Capar, Abdulkerim

PY - 2019/10

Y1 - 2019/10

N2 - Achieving successful results with the sparse representation in super-resolution increases the interest in the field. The sparse representation model, which is an important method in super-resolution, consists of image patches, a correct dictionary and a sparse linear combination of the elements of this dictionary. At this point, the super-resolution successfully reflects the sparse pattern by obtaining high-resolution images with the sparse pattern from low-resolution image patches. The detection of image regions is critical here. In the proposed method, the successes of the results are compared by using Fuzzy C-Means Clustering and Hue-Saturation-Value (HSV) Based Segmentation methods for determination of these regions.

AB - Achieving successful results with the sparse representation in super-resolution increases the interest in the field. The sparse representation model, which is an important method in super-resolution, consists of image patches, a correct dictionary and a sparse linear combination of the elements of this dictionary. At this point, the super-resolution successfully reflects the sparse pattern by obtaining high-resolution images with the sparse pattern from low-resolution image patches. The detection of image regions is critical here. In the proposed method, the successes of the results are compared by using Fuzzy C-Means Clustering and Hue-Saturation-Value (HSV) Based Segmentation methods for determination of these regions.

KW - Dictionary

KW - Fuzzy c-means clustering

KW - Hsv segmentation

KW - Image processing

KW - Sparse representation

KW - Superresolution

UR - http://www.scopus.com/inward/record.url?scp=85075612569&partnerID=8YFLogxK

U2 - 10.1109/TIPTEKNO.2019.8895204

DO - 10.1109/TIPTEKNO.2019.8895204

M3 - Conference contribution

AN - SCOPUS:85075612569

T3 - TIPTEKNO 2019 - Tip Teknolojileri Kongresi

BT - TIPTEKNO 2019 - Tip Teknolojileri Kongresi

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 Medical Technologies Congress, TIPTEKNO 2019

Y2 - 3 October 2019 through 5 October 2019

ER -

Detection methods of salient regions in super-resolution based on sparse representation

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this