Open AccessSpeckle noise reduction algorithm with total variation regularization in optical coherence tomography
Author(s) -
G. Gong,
Hongming Zhang,
Minyu Yao
Publication year - 2015
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.23.024699
Subject(s) - optical coherence tomography , speckle noise , image quality , speckle pattern , algorithm , computer science , noise reduction , multiplicative function , regularization (linguistics) , preprocessor , optics , multiplicative noise , contrast to noise ratio , artificial intelligence , mathematics , physics , image (mathematics) , transmission (telecommunications) , telecommunications , mathematical analysis , signal transfer function , analog signal
Optical coherence tomography (OCT) is an important imaging technique extensively applied in medical sciences. However, OCT images often suffer from speckle noise, which is a kind of multiplicative noise inherited in coherent imaging systems. A speckle noise reduction algorithm with total variation (TV) regularization is proposed to restore speckled OCT images. It constructs the regularization parameter and utilizes the tuning function. The proposed algorithm realizes sufficient speckle noise reduction and delicate edge preservation. Simulations demonstrate the performance of the proposed algorithm with respect to visual effects, processing time and image quality metrics of signal-to-noise ratio (SNR), equivalent number of looks (ENL), contrast-to-noise ratio (CNR), relative mean square error (RMSE) and edge preservation factor. Compared with some classical and typical despeckling algorithms, the proposed algorithm exhibits good results in edge preservation, recovery error and time efficiency, and presents better SNR, ENL and CNR. The applicability of the proposed algorithm with regard to OCT in-device preprocessing is discussed in details. Therefore, it promotes the application of OCT imaging in clinical diagnosis and analysis.