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Author:

Wang, Meng (Wang, Meng.) | Ning, Zhen Hu (Ning, Zhen Hu.) | Yu, Jing (Yu, Jing.) | Xiao, Chuang Bai (Xiao, Chuang Bai.)

Indexed by:

EI Scopus SCIE

Abstract:

Compressed sensing in image reconstruction has attracted attention and many studies are proposed. As we know, adding prior knowledge about the distribution of the support on the original signal to CS can improve the quality of reconstruction. However, it is still difficult for a recovery framework adjusts its strategy for exploiting the prior knowledge efficiently according to the current estimated signals in serial iterations. With the theory of information geometry, we propose an adaptive strategy based on the current estimated signal in each iteration of the recovery. We also improve the performance of existing algorithms through the adaptive strategy for exploiting the prior knowledge according to the current estimated signal. Simulations are presented to validate the results. In the end, we also show the application of the model in the image.

Keyword:

Image Reconstruction Information Geometry Compressed Sensing

Author Community:

  • [ 1 ] [Wang, Meng]Beijing Informat Sci & Technol Univ, Sch Appl Sci, Beijing 100192, Peoples R China
  • [ 2 ] [Ning, Zhen Hu]Beijing Univ Technol, Fac Informat Technol, Coll Comp Sci, Beijing 100124, Peoples R China
  • [ 3 ] [Yu, Jing]Beijing Univ Technol, Fac Informat Technol, Coll Comp Sci, Beijing 100124, Peoples R China
  • [ 4 ] [Xiao, Chuang Bai]Beijing Univ Technol, Fac Informat Technol, Coll Comp Sci, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Ning, Zhen Hu]Beijing Univ Technol, Fac Informat Technol, Coll Comp Sci, Beijing 100124, Peoples R China

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Source :

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS

ISSN: 1976-7277

Year: 2021

Issue: 2

Volume: 15

Page: 461-484

1 . 5 0 0

JCR@2022

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:87

JCR Journal Grade:4

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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