Paper Info
Title
Mixture Noise Removal In Ultrasound Images Using Scobep And Low-Rank Matrix Completion
Abstract
Denoising as one of the most significant tools in ultrasound imaging was studied widely in the literature. However, most existing ultrasound image denoising algorithms have assumed the additive white Gaussian noise. In this work, we propose two efficient ultrasound image denoising methods that can handle a noise mixture of various types. Our methods are based on SCoBeP [1] and low-rank matrix completion as follows. In our first method, a noisy image is processed in blockwise manner and for each processed block we find candidate match pixels on other images using sparse coding and belief propagation, where in our second algorithm, we use overlapped patches to further lower the computation complexity. The blocks centered around these candidate pixels then will stack together and unreliable pixels will be removed using fast matrix completion method [2]. We demonstrate the effectiveness of our algorithm in removing the mixed noise through the results. We also compare with other denoising technique using matrix completion. Our methods results in comparable performance with significantly lower computation complexity.
Year
DOI
Venue
2013
10.1109/EMBC.2013.6609449
2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Keywords
Field
DocType
additive white gaussian noise,vectors,belief propagation,matrix decomposition,feature extraction,sparse coding,noise reduction,noise
Noise reduction,Computer vision,Pattern recognition,Matrix completion,Computer science,Non-local means,Matrix decomposition,Low-rank approximation,Pixel,Artificial intelligence,Video denoising,Additive white Gaussian noise
Conference
Volume
ISSN
Citations 
2013
1557-170X
2
PageRank 
References 
Authors
0.38
11
4
Name
Order
Citations
PageRank
Nafise Barzigar1313.71
Aminmohammad Roozgard2364.23
Pramode Verma3428.33
Samuel Cheng4496.18