NDLI: Iterative weighted sparse representation for X-ray cardiovascular angiogram image denoising over learned dictionary

Content Provider	IET Digital Library
Author	Huang, Zhenghua Li, Qian Zhang, Tianxu Sang, Nong Hong, Hanyu
Abstract	Non-local self-similar patch-based denoising techniques have been viewed as the most popular denoising approaches in computer vision. This study has proposed a novel iterative weighted sparse representation (IWSR) scheme for X-ray cardiovascular angiogram image denoising. The main procedures of this scheme include four parts. First, a maximum a posterior (MAP) distribution by the Bayes’ theory is adopted to simultaneously estimate the estimated image and sparse representation with different Gaussian distributions approximating to likelihood prior, non-local self-similar patch prior and sparse representation prior. Second, the MAP problem is converted to minimise an energy function using the logarithmic transformation. Third, the function is efficiently solved by the single and effective alternating directions method of multipliers algorithm along with singular value decomposition (SVD) algorithm. Finally, owing to learned dictionary by K-SVD algorithm, the qualitative and quantitative results of widely synthetic experiments demonstrate that the proposed IWSR denoising method performs effectively and can obtain competitive denoising performance and high-quality images compared with those advanced denoising methods. The results of extensive experiments on clinical X-ray angiogram images further illustrate that the IWSR method performs well on noise reduction and vascular structures including edges and capillaries preservation, integral cardiovascular trees of which are beneficial for clinicians to diagnose and analyse cardiovascular diseases.
Starting Page	254
Ending Page	261
Page Count	8
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 2, Feb (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0518
Journal	IET Image Processing
Publisher Date	2017-10-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Advanced Denoising Method Algebra Bayes Method Bayes Theory Biology And Medical Computing Biomedical Imaging/measurement Capillaries Preservation Cardiovascular Diseases Cardiovascular System Combinatorial Mathematics Computer Vision And Image Processing Technique Diagnostic Radiography Diseases Effective Alternating Directions Method Energy Function Gaussian Distribution Graph Theory High-quality Image Image Denoising Image Representation Integral Cardiovascular Trees Interpolation And Function Approximation Iterative Method Iterative Weighted Sparse Representation IWSR Method IWSR Scheme K-SVD Algorithm Learned Dictionary Linear Algebra Logarithmic Transformation MAP Problem Mathematics Maximum A Posterior Distribution Medical Image Processing Multipliers Algorithm Noise Reduction Nonlocal Self-similar Patch Prior Nonlocal Self-similar Patch-based Denoising Technique Numerical Analysis Numerical Approximation And Analysis Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Probability Theory Radiography And Computed Tomography Set Theory Singular Value Decomposition Singular Value Decomposition Algorithm Sparse Representation Prior Statistics Stochastic Linearised SCUC Trees Vascular Structure X-Ray Cardiovascular Angiogram Image Denoising X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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