期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 32, 期 11, 页码 2034-2049出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2013.2271904
关键词
Fast retina scanning; image enhancement; optical coherence tomography; simultaneous denoising and interpolation; sparse representation
类别
资金
- American Health Assistance Foundation
- U.S. Army Medical Research Acquisition Activity [W81XWH-12-1-0397]
- Young Teacher Growth Plan, National Natural Science Foundation of China
- Young Teacher Growth Plan
- National Natural Science Foundation of China [61172161]
- National Natural Science Fund for Distinguished Young Scholars of China
- Young Teacher Growth Plan, Hunan University [531107040725]
- Chinese Ministry of Education
- NIH [P30 EY-005722, R01 EY022691]
In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods.
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