1. Iterative weighted sparse representation for X‐ray cardiovascular angiogram image denoising over learned dictionary.
- Author
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Huang, Zhenghua, Li, Qian, Zhang, Tianxu, Sang, Nong, and 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. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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