Image Processing

Patch-based denoising method using low-rank technique and targeted database for optical coherence tomography image

[+] Author Affiliations
Xiaoming Liu, Zhou Yang, Jia Wang, Jun Liu, Kai Zhang, Wei Hu

Wuhan University of Science and Technology, College of Computer Science and Technology, Wuhan, China

Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, China

J. Med. Imag. 4(1), 014002 (Feb 01, 2017). doi:10.1117/1.JMI.4.1.014002
History: Received June 2, 2016; Accepted January 16, 2017
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Abstract.  Image denoising is a crucial step before performing segmentation or feature extraction on an image, which affects the final result in image processing. In recent years, utilizing the self-similarity characteristics of the images, many patch-based image denoising methods have been proposed, but most of them, named the internal denoising methods, utilized the noisy image only where the performances are constrained by the limited information they used. We proposed a patch-based method, which uses a low-rank technique and targeted database, to denoise the optical coherence tomography (OCT) image. When selecting the similar patches for the noisy patch, our method combined internal and external denoising, utilizing the other images relevant to the noisy image, in which our targeted database is made up of these two kinds of images and is an improvement compared with the previous methods. Next, we leverage the low-rank technique to denoise the group matrix consisting of the noisy patch and the corresponding similar patches, for the fact that a clean image can be seen as a low-rank matrix and rank of the noisy image is much larger than the clean image. After the first-step denoising is accomplished, we take advantage of Gabor transform, which considered the layer characteristic of the OCT retinal images, to construct a noisy image before the second step. Experimental results demonstrate that our method compares favorably with the existing state-of-the-art methods.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Topics

Denoising ; Databases

Citation

Xiaoming Liu ; Zhou Yang ; Jia Wang ; Jun Liu ; Kai Zhang, et al.
"Patch-based denoising method using low-rank technique and targeted database for optical coherence tomography image", J. Med. Imag. 4(1), 014002 (Feb 01, 2017). ; http://dx.doi.org/10.1117/1.JMI.4.1.014002


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