A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method
Speckle noise removal in medical ultrasound images is a challenging task. In this paper, a new model is proposed to removal speckle noise, alternating direction method of multipliers algorithm is employed to solve the new energy minimization model. The convexity, existence, and uniqueness of the new...
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Format: | Article |
Language: | English |
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Wiley
2020-01-01
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Series: | Journal of Function Spaces |
Online Access: | http://dx.doi.org/10.1155/2020/3416958 |
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author | Bo Chen Yan Lv Jinbin Zou Wensheng Chen Binbin Pan |
author_facet | Bo Chen Yan Lv Jinbin Zou Wensheng Chen Binbin Pan |
author_sort | Bo Chen |
collection | DOAJ |
description | Speckle noise removal in medical ultrasound images is a challenging task. In this paper, a new model is proposed to removal speckle noise, alternating direction method of multipliers algorithm is employed to solve the new energy minimization model. The convexity, existence, and uniqueness of the new energy minimization model’s solution are proved. Series of experiments are designed in this paper. Numerical results show that the new algorithm can reduce the step effect effectively obtain good results in visual effect and quantitative measures by comparing with some traditional models. |
format | Article |
id | doaj-art-e74b7bae6c994cdd95f9229db2a51f1c |
institution | Kabale University |
issn | 2314-8896 2314-8888 |
language | English |
publishDate | 2020-01-01 |
publisher | Wiley |
record_format | Article |
series | Journal of Function Spaces |
spelling | doaj-art-e74b7bae6c994cdd95f9229db2a51f1c2025-02-03T06:46:32ZengWileyJournal of Function Spaces2314-88962314-88882020-01-01202010.1155/2020/34169583416958A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization MethodBo Chen0Yan Lv1Jinbin Zou2Wensheng Chen3Binbin Pan4Shenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, ChinaShenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, ChinaShenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, ChinaShenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, ChinaShenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, ChinaSpeckle noise removal in medical ultrasound images is a challenging task. In this paper, a new model is proposed to removal speckle noise, alternating direction method of multipliers algorithm is employed to solve the new energy minimization model. The convexity, existence, and uniqueness of the new energy minimization model’s solution are proved. Series of experiments are designed in this paper. Numerical results show that the new algorithm can reduce the step effect effectively obtain good results in visual effect and quantitative measures by comparing with some traditional models.http://dx.doi.org/10.1155/2020/3416958 |
spellingShingle | Bo Chen Yan Lv Jinbin Zou Wensheng Chen Binbin Pan A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method Journal of Function Spaces |
title | A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method |
title_full | A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method |
title_fullStr | A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method |
title_full_unstemmed | A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method |
title_short | A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method |
title_sort | novel speckle noise removal algorithm based on admm and energy minimization method |
url | http://dx.doi.org/10.1155/2020/3416958 |
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