2005
DOI: 10.1016/j.optcom.2004.08.039
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Improvement of the LLS and MAP deconvolution algorithms by automatic determination of optimal regularization parameters and pre-filtering of original data

Abstract: We show that automatic determination of regularization threshold and pre-filtering of 3-D fluorescence microscopic images improves the stability of deconvolution results when using the Linear Least squares Solution or the Maximum a Posteriori method. Doing so, the choice of the regularization parameter much less depends on a priori knowledge of the specimen or skills of the operator. This increases the reliability and repeatability of quantitative measurements on deconvolved images.

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Cited by 12 publications
(9 citation statements)
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“…Note however that in tomography with specimen rotation, this axis of lower image quality corresponds to the physical rotation axis of the specimen, while in conventional microscopy, the optical axis is the direction of worst imaging conditions. Note also that the observed elongation along the rotation axis in diffractive tomography with sample rotation is less pronounced than that observed in standard transmission microscopy or in fluorescence microscopy along the optical axis [13,14]. This can be easily understood if one considers the so-called missing-cone in transmission or fluorescence microscopy [15]: while high frequencies are captured along lateral axes, no frequencies are measured along the optical axis, and even the maximum thickness of the captured frequency support along this direction is several times smaller than its lateral extension.…”
Section: Simulationsmentioning
confidence: 84%
“…Note however that in tomography with specimen rotation, this axis of lower image quality corresponds to the physical rotation axis of the specimen, while in conventional microscopy, the optical axis is the direction of worst imaging conditions. Note also that the observed elongation along the rotation axis in diffractive tomography with sample rotation is less pronounced than that observed in standard transmission microscopy or in fluorescence microscopy along the optical axis [13,14]. This can be easily understood if one considers the so-called missing-cone in transmission or fluorescence microscopy [15]: while high frequencies are captured along lateral axes, no frequencies are measured along the optical axis, and even the maximum thickness of the captured frequency support along this direction is several times smaller than its lateral extension.…”
Section: Simulationsmentioning
confidence: 84%
“…(8). Often, additional assumptions (noise's origin or amplitude) [2,3,7] or iterative methods serve to improve intensity based deconvolution [5,6]. The presented complex deconvolution foregoes any assumptions since it is simply based on inverting image formation from Eq.…”
Section: Inverse Filter Deconvolution Of Complex Fieldsmentioning
confidence: 99%
“…Many 2D deconvolution methods, like deblurring, can be applied to improve image quality of incoherent imaging systems [4] and 3D deconvolution techniques give rise to enhanced optical sectioning capability [2]. Based on iterative expectation-maximization algorithms for maximum-likelihood deconvo-lution of incoherent images, even enhanced resolution has been demonstrated [5,6] at the cost of computational power. All such efforts make deconvolution a common post-processing method for biological applications such as deconvolution of fluorescence microscopy images [7].…”
Section: Introductionmentioning
confidence: 99%
“…Indeed, catastrophic amplification of noise may occur, which may render the deconvolution result useless. An important point in order to get optimal results is to determine the optimal level of regularization of the deconvolution process with respect to the noise present in the image [26].…”
Section: M a G E P R O C E S S I N G : M U L T I -K E R N E L D E Cmentioning
confidence: 99%