Denoising optical coherence tomography using second order total generalized variation decomposition. (February 2016)

• Record Type: Journal Article
• Authors: Duan, Jinming; Lu, Wenqi; Tench, Christopher; Gottlob, Irene; Proudlock, Frank; Samani, Niraj Nilesh; Bai, Li
• Is Part Of: Biomedical signal processing and control. Volume 24:(2015)
• Page Start: 120
• Page End: 127
• Publication Date: 2016-02
• Subjects: Optical coherence tomography -- Variational methods -- Image decomposition -- Noise removal -- Total generalised variation -- Split Bregman algorithm -- Fast Fourier transform; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.09.012
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Highlights: A novel image decomposition model is proposed for OCT image denoising. The incorporation of TGV regularisation allows the absence of staircase effect. The FFT-based split Bregman algorithm is developed to solve the model explicitly. Abstract: 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.