2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings
DOI: 10.1109/icassp.2006.1660551
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Multi-Dimensional Denoising of Real-Time Oct Imaging Data

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Cited by 6 publications
(4 citation statements)
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“…Multiple manuscripts in the literature have now specifically used noise distributions for OCT denoising [41], [42]. Both Ralston et al [41] and Yin et al [42] derive OCT denoising methods by modeling the noise as Gaussian in the log domain. However, the Gaussian distribution has not been empirically and statistically validated in the retina.…”
Section: Introductionmentioning
confidence: 99%
“…Multiple manuscripts in the literature have now specifically used noise distributions for OCT denoising [41], [42]. Both Ralston et al [41] and Yin et al [42] derive OCT denoising methods by modeling the noise as Gaussian in the log domain. However, the Gaussian distribution has not been empirically and statistically validated in the retina.…”
Section: Introductionmentioning
confidence: 99%
“…The logarithm of the magnitude is processed because speckle can be modeled as multiplicative noise to the image, which becomes additive noise once the logarithm is taken. Additionally, a multidimensional wavelet method utilizes correlations in time-series data to suppress noise via decorrelation [7]. An example of denoising using this technique is shown in Fig.…”
Section: Speckle Reduction and Signal Improvementmentioning
confidence: 99%
“…The Gaussian assumption has been shown to be sufficiently accurate for most practical applications. Based on this assumption, Ralston et al [17] showed successful denoising results on experimental data. Also, in practice, the noise floor is about 40 dB below the signal level (small-noise regime) [15], [18], [19]; i.e.,…”
Section: Bias and Variance Calculationsmentioning
confidence: 99%