A novel weighted sparse representation denoising method for X-ray cardiovascular angiogram image

摘要

X-ray angiogram image denoising is always an active research topic in the field of computer vision. In particular, the denoising performance of many existing methods had been greatly improved by the widely use of nonlocal similar patches. In this paper, the iterative procedure of the restored image is considered to be a Markov process, then a proposed image denoising model is constructed based on generalized Bayes' rule with the prior knowledge including global image information, non-local self-similar patches and sparse representation. The single and effectively alternating directions method of multipliers (ADMM) algorithm along with singular value decomposition (SVD) algorithm is used to solve the image denoising model. The results of widely synthetic experiments demonstrate that, owing to K-SVD dictionary, the weighted sparse representation denoising (WSRD) method, which performs effectively, obtain competitive denoising performance and high-quality images. Moreover, the restored results of clinical X-ray cardiovascular angiogram images further illustrate that our proposed method can suppress noise and preserve the vascular structures including edges and capillaries well.