Denoising optical coherence tomography using second order total generalized variation decomposition

被引:77
作者
Duan, Jinming [1 ]
Lu, Wenqi [2 ]
Tench, Christopher [3 ]
Gottlob, Irene [4 ]
Proudlock, Frank [4 ]
Samani, Niraj Nilesh [4 ]
Bai, Li [1 ]
机构
[1] Univ Nottingham, Sch Comp Sci, Nottingham NG7 2RD, England
[2] Qingdao Univ, Coll Informat Engn, Qingdao, Peoples R China
[3] Univ Nottingham, Sch Med, Nottingham NG7 2RD, England
[4] Univ Leicester, Dept Ophthalmol, Leicester LE1 7RH, Leics, England
关键词
Optical coherence tomography; Variational methods; Image decomposition; Noise removal; Total generalised variation; Split Bregman algorithm; Fast Fourier transform; SPECKLE NOISE-REDUCTION; FAST ALGORITHM; IMAGES;
D O I
10.1016/j.bspc.2015.09.012
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
In this paper, we apply image decomposition for image denoising by considering the speckle noise in the (OCT) image as texture or oscillatory patterns. A novel second order total generalised variation (TGV) decomposition model is proposed to remove noise (texture) from the OCT image. The incorporation of the TGV regularisation in the proposed model can eliminate the staircase side effect in the resulting denoised image (structure). By introducing auxiliary splitting variables and Bregman iterative parameters, a fast Fourier transform based split Bregman algorithm is developed to solve the proposed model explicitly and efficiently. Extensive experiments are conducted on both synthetic and real OCT images to demonstrate that the proposed model outperforms state-of-the-art speckle noise reduction methods. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:120 / 127
页数:8
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