|Table of Contents|

Adaptive weighted undersampling image reconstruction algorithm

《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

Issue:
2020年02期
Page:
209-215
Research Field:
Publishing date:

Info

Title:
Adaptive weighted undersampling image reconstruction algorithm
Author(s):
Ban Xiaozheng; Li Zhihua
School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China
Keywords:
compressed sensing;  image reconstruction;  iterative support detection;  adaptive weighted
PACS:
TP393
DOI:
10.14177/j.cnki.32-1397n.2020.44.02.012
Abstract:
Aiming at the problem that TV regularized image reconstruction is easy to be sensitive to noise and artifacts in under-sampling environment,a dual regularized adaptive weighted image reconstruction model combining the discrete wavelet and the TV is constructed. Based on this model,an adaptive weighted iterative reconstruction algorithm is proposed. In each iteration,the algorithm calculates the TV regularization term and the wavelet coefficient prior term by the threshold shrinkage method,and then updates the reconstructed image. In order to improve the quality of the reconstructed image,an iterative support detection method is introduced to calculate the adaptive weight of the wavelet coefficient. The experimental results show that the proposed algorithm can achieve better overall performance in terms of time efficiency and reconstruction quality than other similar algorithms.

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Last Update: 2020-04-20