title: CT image sequence restoration based on sparse and low-rank decomposition.
abstract: Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT) image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA), Linearized Alternating Direction Method with Adaptive Penalty (LADMAP) and Go Decomposition (GoDec). Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images.
authors.affiliation: Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi,China.
authors.forename: Shuiping
authors.initials: S
authors.lastname: Gou
authors.forename: Yueyue
authors.initials: Y
authors.lastname: Wang
authors.forename: Zhilong
authors.initials: Z
authors.lastname: Wang
authors.forename: Yong
authors.initials: Y
authors.lastname: Peng
authors.forename: Xiaopeng
authors.initials: X
authors.lastname: Zhang
authors.forename: Licheng
authors.initials: L
authors.lastname: Jiao
authors.forename: Jianshe
authors.initials: J
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*
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