29 August 2016 Two-dimensional noise reduction in optical coherence tomography based on the shearlet transform
Author Affiliations +
Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100331U (2016) https://doi.org/10.1117/12.2244630
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
Image denoising is a very important step in image processing. In recent years, a lot of image denoising algorithms have been proposed, several of them are transform domain based methods, such as wavelet, contourlet, and shearlet. Shearlet is a new type of multiscale geometric analysis tool, which can obtain a sparse representation of the image and produce the optimal approximation. The transform generates shearlet functions with different features by scaling, shearing, and translation of the basic functions. In this paper, we introduced shearlet transformation into optical coherence tomography images to reduce noise, and proposed a multiscale, directional adapted speckle reduction method. Experiment results showed the effectiveness of the proposed method.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaoming Liu, Zhou Yang, Jia Wang, Zhigang Zeng, Zuoyong Li, "Two-dimensional noise reduction in optical coherence tomography based on the shearlet transform", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331U (29 August 2016); doi: 10.1117/12.2244630; https://doi.org/10.1117/12.2244630
PROCEEDINGS
5 PAGES


SHARE
Back to Top