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Sparse representation based medical ultrasound images denoising with reshaped-RED
Author(s): Xiaoqiu Pu; Zhixin Li; Baopeng Li; Hao Lei; Wei Gao; Jiwei Liu
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Paper Abstract

Medical ultrasound images are usually corrupted by the noise during their acquisition known as speckle. Speckle noise removal is a key stage in medical ultrasound image processing. Due to the ill-posed feature of image denoising, many regularization methods have been proved effective. This paper introduces an approach which collaborate both sparse dictionary learning and regularization method to remove the speckle noise. The method trains a redundant dictionary by an efficient dictionary learning algorithm, and then uses it in an image prior regularization model to obtain the recovered image. Experimental results demonstrate that the proposed model has enhanced performance both in despeckling and texture-preserving of medical ultrasound images compared to some popular methods.

Paper Details

Date Published: 14 August 2019
PDF: 11 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790K (14 August 2019); doi: 10.1117/12.2540245
Show Author Affiliations
Xiaoqiu Pu, Xi'an Institute of Optics and Precision Mechanics of CAS (China)
Univ. of Chinese Academy of Sciences (China)
Zhixin Li, Xi'an Institute of Optics and Precision Mechanics of CAS (China)
Univ. of Chinese Academy of Sciences (China)
Baopeng Li, Xi'an Institute of Optics and Precision Mechanics of CAS (China)
Hao Lei, Xi'an Institute of Optics and Precision Mechanics of CAS (China)
Wei Gao, Xi'an Institute of Optics and Precision Mechanics of CAS (China)
Jiwei Liu, Xi'an Institute of Optics and Precision Mechanics of CAS (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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