Paper
26 September 2013 Optical Coherence Tomography noise reduction over learned dictionaries with introduction of complex wavelet for start dictionary
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Abstract
Optical Coherence Tomography (OCT) suffers from speckle noise which causes erroneous interpretation. OCT denoising methods may be studied in "raw image domain" and "sparse representation". Comparison of mentioned denoising strategies in magnitude domain shows that wavelet-thresholding methods had the highest ability, among which wavelets with shift invariant property yielded better results. We chose dictionary learning to improve the performance of available wavelet-thresholding by tailoring adjusted dictionaries instead of using pre-defined bases. Furthermore, in order to take advantage of shift invariant wavelets, we introduce a new scheme in dictionary learning which starts from a dual tree complex wavelet. we investigate the performance of different speckle reduction methods: 2D Conventional Dictionary Learning (2D CDL), Real part of 2D Dictionary Learning with start dictionary of dual-tree Complex Wavelet Transform (2D RCWDL) and Imaginary part of 2D Dictionary Learning with start dictionary of dual-tree Complex Wavelet Transform (2D ICWDL). It can be seen that the performance of the proposed method in 2D R/I CWDL are considerably better than other methods in CNR.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Raheleh Kafieh and Hossein Rabbani "Optical Coherence Tomography noise reduction over learned dictionaries with introduction of complex wavelet for start dictionary", Proc. SPIE 8858, Wavelets and Sparsity XV, 885826 (26 September 2013); https://doi.org/10.1117/12.2026520
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Cited by 5 scholarly publications.
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KEYWORDS
Associative arrays

Optical coherence tomography

Denoising

Wavelets

Continuous wavelet transforms

Wavelet transforms

Image filtering

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