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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 contributionSeyrek gösterime dayali süper-çözünürlükte belirgin bölgelerin tespit metotlari
Original languageEnglish
Title of host publicationTIPTEKNO 2019 - Tip Teknolojileri Kongresi
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728124209
DOIs
Publication statusPublished - Oct 2019
Event2019 Medical Technologies Congress, TIPTEKNO 2019 - Izmir, Turkey
Duration: 3 Oct 20195 Oct 2019

Publication series

NameTIPTEKNO 2019 - Tip Teknolojileri Kongresi

Conference

Conference2019 Medical Technologies Congress, TIPTEKNO 2019
Country/TerritoryTurkey
CityIzmir
Period3/10/195/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

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

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