NDLI: A sparse representation and dictionary learning based algorithm for image restoration in the presence of Rician noise

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A sparse representation and dictionary learning based algorithm for image restoration in the presence of Rician noise

Content Provider	Scilit
Author	Chen, Wensheng You, Jie Chen, Bo Pan, Binbin Li, Lihong Pomeroy, Marc Liang, Zhengrong
Copyright Year	2018
Description	Journal: Neurocomputing Rician noise removal for Magnetic Resonance Imaging (MRI) is very important because the MRI has been widely used in various clinical applications and the associated Rician noise deteriorates the image quality and causes errors in interpreting the images. Great efforts have recently been devoted to develop the corresponding noise-removal algorithms, particularly the development based on the newly-established Total Variation (TV) theorem. However, all the TV-based algorithms depend mainly on the gradient information and have been shown to produce the so called “blocky” artifact, which also deteriorates the image quality and causes image interpretation errors. In order to avoid producing the artifact, this paper presents a new de-noising model based on sparse representation and dictionary learning. The Split Bregman Iteration strategy is employed to implement the model. Furthermore, an appropriate dictionary is designed by the use of the Kernel Singular Value Decomposition method, resulting in a new Rician noise removal algorithm. Compared with other de-noising algorithms, the presented new algorithm can achieve superior performance, in terms of quantitative measures of the Structural Similarity Index and Peak Signal to Noise Ratio, by a series of experiments using different images in the presence of Rician noise.
Related Links	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6133329/pdf
Ending Page	140
Page Count	11
Starting Page	130
ISSN	09252312
DOI	10.1016/j.neucom.2018.01.066
Journal	Neurocomputing
Volume Number	286
Language	English
Publisher	Elsevier BV
Publisher Date	2018-02-02
Access Restriction	Open
Subject Keyword	Journal: Neurocomputing Sparse Representations
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Cognitive Neuroscience Computer Science Applications

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