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Image super resolution by sparse linear regression and iterative back projection

  • Mehmood Nawaz
  • , Rong Xie
  • , Liang Zhang
  • , Malik Asfandyar
  • , Muddsser Hussain

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

This paper presents a method which focus on the increase of visual quality of SR image reconstructed from input low resolution image. Similar to the framework as exploited in [1], a modified algorithm is developed which is based on sparse linear regression and iterative back projection. Different from the techniques used in [1] [6], a feature sign search algorithm [17] is used to find the relevant features of the regression function under a priori assumption. Furthermore, a modified Gaussian high pass filter is additionally used for the refinement of the initial reconstructed SR image through iterative back-projection technique to reduce visual artifacts. Experimental results conclude that this modified approach achieves better quality of reconstructed SR images than the other similar SR methods.
Original languageEnglish
Title of host publicationIEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB
PublisherIEEE
ISBN (Electronic)9781467390446
ISBN (Print)9781467390453
DOIs
Publication statusPublished - Jun 2016
Externally publishedYes
Event2016 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) - Nara, Japan
Duration: 1 Jun 20163 Jun 2016

Publication series

NameIEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB
Volume2016-July
ISSN (Electronic)2155-5052

Conference

Conference2016 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
PlaceJapan
CityNara
Period1/06/163/06/16

Research Keywords

  • Image Super Resolution
  • back projection
  • sparse representation

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