Next Article in Journal
A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion
Previous Article in Journal
Estimating Spatiotemporal Information from Behavioral Sensing Data of Wheelchair Users by Machine Learning Technologies
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Information
Volume 10
Issue 3
10.3390/info10030115
information-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

An Efficient Image Reconstruction Framework Using Total Variation Regularization with Lp-Quasinorm and Group Gradient Sparsity

by
Fan Lin
*,
Yingpin Chen
,
Lingzhi Wang
,
Yuqun Chen
,
Wei Zhu
and
Fei Yu
School of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China
*
Author to whom correspondence should be addressed.
Information 2019, 10(3), 115; https://doi.org/10.3390/info10030115
Submission received: 22 February 2019 / Revised: 12 March 2019 / Accepted: 13 March 2019 / Published: 16 March 2019
(This article belongs to the Section Information Processes)
Browse Figures
Versions Notes

Abstract

The total variation (TV) regularization-based methods are proven to be effective in removing random noise. However, these solutions usually have staircase effects. This paper proposes a new image reconstruction method based on TV regularization with Lp-quasinorm and group gradient sparsity. In this method, the regularization term of the group gradient sparsity can retrieve the neighborhood information of an image gradient, and the Lp-quasinorm constraint can characterize the sparsity of the image gradient. The method can effectively deblur images and remove impulse noise to well preserve image edge information and reduce the staircase effect. To improve the image recovery efficiency, a Fast Fourier Transform (FFT) is introduced to effectively avoid large matrix multiplication operations. Moreover, by introducing accelerated alternating direction method of multipliers (ADMM) in the method to allow for a fast restart of the optimization process, this method can run faster. In numerical experiments on standard test images sourced form Emory University and CVG-UGR (Computer Vision Group, University of Granada) image database, the advantage of the new method is verified by comparing it with existing advanced TV-based methods in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and operational time.
Keywords: total variation; group gradient sparsity; Lp-quasinorm; accelerated alternating direction method of multipliers; image reconstruction total variation; group gradient sparsity; Lp-quasinorm; accelerated alternating direction method of multipliers; image reconstruction

Share and Cite

MDPI and ACS Style

Lin, F.; Chen, Y.; Wang, L.; Chen, Y.; Zhu, W.; Yu, F. An Efficient Image Reconstruction Framework Using Total Variation Regularization with Lp-Quasinorm and Group Gradient Sparsity. Information 2019, 10, 115. https://doi.org/10.3390/info10030115

AMA Style

Lin F, Chen Y, Wang L, Chen Y, Zhu W, Yu F. An Efficient Image Reconstruction Framework Using Total Variation Regularization with Lp-Quasinorm and Group Gradient Sparsity. Information. 2019; 10(3):115. https://doi.org/10.3390/info10030115

Chicago/Turabian Style

Lin, Fan, Yingpin Chen, Lingzhi Wang, Yuqun Chen, Wei Zhu, and Fei Yu. 2019. "An Efficient Image Reconstruction Framework Using Total Variation Regularization with Lp-Quasinorm and Group Gradient Sparsity" Information 10, no. 3: 115. https://doi.org/10.3390/info10030115

APA Style

Lin, F., Chen, Y., Wang, L., Chen, Y., Zhu, W., & Yu, F. (2019). An Efficient Image Reconstruction Framework Using Total Variation Regularization with Lp-Quasinorm and Group Gradient Sparsity. Information, 10(3), 115. https://doi.org/10.3390/info10030115

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop