Fast Haar-Wavelet Denoising Of Multidimensional Fluorescence Microscopy Data

We propose a novel denoising algorithm to reduce the Poisson noise that is typically dominant in fluorescence microscopy data. To process large datasets at a low computational cost, we use the unnormalized Haar wavelet transform. Thanks to some of its appealing properties, independent unbiased MSE estimates can be derived for each subband. Based on these Poisson unbiased MSE estimates, we then optimize linearly parametrized interscale thresholding. Correlations between adjacent images of the multidimensional data are accounted for through a sliding window approach. Experiments on simulated and real data show that the proposed solution is qualitatively similar to a state-of-the-art multiscale method, while being orders of magnitude faster.

Published in:
2009 Ieee International Symposium On Biomedical Imaging: From Nano To Macro, Vols 1 And 2, 310-313
Presented at:
IEEE Internaional Symposium on Biomedical Imaging - From Nano to Macro, Boston, MA, Jun 28-Jul 01, 2009
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa

 Record created 2010-11-30, last modified 2018-11-14

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