Complexity reduction in multi-dictionary based single-image superresolution reconstruction via pahse congtuency

Yu ZHOU, Sam KWONG, Wei GAO, Xiao ZHANG, Xu WANG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

6 Citations (Scopus)

Abstract

Compared with single dictionary, multi-dictionary method can achieve better performance in image superresolution reconstruction (SR). However, the computational cost of multi-dictionary based SR is very heavy and usually time-consuming and resource-intensive. In this paper, we proposed a complexity reduction method in multi-dictionary based SR via phase congruency. The PC map of the LR image is extracted and binarized to distinct the importance of the image patches of it. Then, the corresponding important HR patches are reconstructed by multi-dictionary based SR method and the unimportant ones by single-dictionary based SR. The finalized reconstructed HR image is obtained by averaging the overlapped region between the adjacent patches. Experimental results show that our method can not only obtain competitive results but also can save much time and reduce the computational complexity in the reconstruction process compared with multi-dictionary based SR method.
Original languageEnglish
Title of host publicationProceedings of 2015 International Conference on Wavelet Analysis and Pattern Recognition
PublisherIEEE
Pages146-151
Number of pages6
ISBN (Print)9781467372244
DOIs
Publication statusPublished - Jul 2015
Externally publishedYes
Event2015 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) - Holiday Inn Guangzhou Shifu, Guangzhou, China
Duration: 12 Jul 201515 Jul 2015

Conference

Conference2015 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)
Country/TerritoryChina
CityGuangzhou
Period12/07/1515/07/15

Keywords

  • Complexity reduction
  • Dictionary
  • Phase Congruency
  • Supperesolution reconstrution

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