Abstract

Medical images are blurred and noised due to various reasons in the acquirement, transmission and storage. In order to improve the restoration quality of medical images, a regular super-resolution restoration algorithm based on fuzzy similarity fusion is proposed. Based on maintained similarity in multiple scales, the fused similarity of the medical images is computed by fuzzy similarity fusion. First, fuzzy similarity is determined by the regional features. The images with certain similarity are obtained according to the maximum value, and the fused image is obtained by all obvious regional features. Then, an adaptive regularized restoration algorithm is employed. In order to ensure the objective function has a global optimal solution, regularized parameters of the global minimum solution of nonlinear function are solved iteratively. Finally, experimental results show that mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the restored image are visibly improved. The restored image also has an obvious improvement in the burr of local edge. Moreover, the algorithm has good stability with significantly enhanced PSNR.

Details

Title
Regularized super-resolution restoration algorithm for single medical image based on fuzzy similarity fusion
Author
Li, Xingying 1 ; Fu, Weina 2   VIAFID ORCID Logo 

1  School of Culture Management, Wuhan University of Communication, Wuhan, China 
2  College of Compute and Information Enginnering, Inner Mongolia Agricultrual University, Hohhot, China; College of Information Science and Engineering, Hunan Normal University, Changsha, China 
Pages
1-11
Publication year
2019
Publication date
Nov 2019
Publisher
Springer Nature B.V.
ISSN
16875176
e-ISSN
16875281
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1186/s13640-019-0483-y
ProQuest document ID
2311178007
Copyright
EURASIP Journal on Image and Video Processing is a copyright of Springer, (2019). All Rights Reserved., © 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.