Multiframe sure-let denoising of timelapse fluorescence microscopy images

Due to the random nature of photon emission and the various internal noise sources of the detectors, real timelapse fluorescence microscopy images are usually modeled as the sum of a Poisson process plus some Gaussian white noise. In this paper, we propose an adaptation of our SURE-LET denoising str...

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Bibliographic Details
Main Authors: Delpretti, S., Luisier, F., Ramani, S., Blu, T., Unser, M.
Format: Conference Proceeding
Language:English
Subjects:
Background noise
Biomedical imaging
Electron microscopy
Fluorescence
Gaussian noise
Image sequences
Noise
Noise reduction
Parameter estimation
Pixel
SURE
Wavelet
White noise
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Summary:Due to the random nature of photon emission and the various internal noise sources of the detectors, real timelapse fluorescence microscopy images are usually modeled as the sum of a Poisson process plus some Gaussian white noise. In this paper, we propose an adaptation of our SURE-LET denoising strategy to take advantage of the potentially strong similarities between adjacent frames of the observed image sequence. To stabilize the noise variance, we first apply the generalized Anscombe transform using suitable parameters automatically estimated from the observed data. With the proposed algorithm, we show that, in a reasonable computation time, real fluorescence timelapse microscopy images can be denoised with higher quality than conventional algorithms.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2008.4540954