Parametric Blind Deconvolution for Confocal Laser Scanning Microscopy

Pankajakshan, Praveen; Zhang, Bo; Blanc‐Féraud, Laure; Kam, Zvi; Olivo‐Marin, Jean‐Christophe; Zerubia, Josiane

doi:10.1109/iembs.2007.4353856

Cited by 19 publications

(33 citation statements)

References 102 publications

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“…These are much larger than their corresponding theoretically expected values [7] [2]. It was verified that the proposed algorithm can not only estimate the actual PSF from the experiments on synthetic data [2], but also provide much better deconvolution results in comparison to theoretical microscope PSF's (generated using the microscope settings). It was noticed that in each slice of the chosen observation data, there was a significant amount of signal contributions from the neighboring slices too.…”

Section: Biological Specimenmentioning

confidence: 86%

“…A separable 3D Gaussian model best describes the PSF, and is chosen as the a priori model. We have shown some experimental results on real data, and the method gives very good deconvolution and a PSF estimation closer to the true value [2]. A way to minimize the deblurring artifacts may be to estimate the hyperparameter of the regularization model.…”

Section: Discussionmentioning

confidence: 99%

“…When the relative difference measure between the successive iterations is smaller than a threshold (t) (see [2]), we stop the algorithm to get the final deconvolved image.…”

Section: Object Restorationmentioning

confidence: 99%

“…Mathematically the process of degradation is characterized by the PSF, which models the propagation and recording of the electromagnetic radiation from a point source. The PSF thus displays a radial diffractive ring pattern [2] (expanding with defocus) that is introduced by the finite-lens aperture. Thus each optical section has also the out-of-focus contributions from other parts of the object.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Blind deconvolution for diffraction-limited fluorescence microscopy

Pankajakshan

Zhang

Blanc‐Féraud

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Optical Sections of biological samples obtained from a fluorescence Confocal Laser Scanning Microscopes (CLSM) are often degraded by out-of-focus blur and photon counting noise. Such physical constraints on the observation are a result of the diffraction-limited nature of the optical system, and the reduced amount of light detected by the photomultiplier respectively. Hence, the image stacks can benefit from postprocessing restoration methods based on deconvolution. The parameters of the acquisition system's Point Spread Function (PSF) may vary during the course of experimentation, and so they have to be estimated directly from the observation data. We describe here an alternate minimization algorithm for the simultaneous blind estimation of the specimen 3D distribution of fluorescent sources and the PSF. Experimental results on real data show that the algorithm provides very good deconvolution results in comparison to theoretical microscope PSF models.

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Section: Biological Specimenmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

“…When the relative difference measure between the successive iterations is smaller than a threshold (t) (see [2]), we stop the algorithm to get the final deconvolved image.…”

Section: Object Restorationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blind deconvolution for diffraction-limited fluorescence microscopy

Pankajakshan

Zhang

Blanc‐Féraud

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Self Cite

View full text Add to dashboard Cite

show abstract

“…Commonly used filters in SIM image reconstruction are borrowed from the field of image restoration, i.e. methods to extend resolution beyond the diffraction limit by incorporating prior knowledge such as nonnegativity [18], noise models [19], background level [20], and image smoothness [21]. The default filtering technique in SIM is (generalized) Wiener filtering [22][23][24], with a signal-tonoise ratio (SNR) that is assumed constant across the entire spatial frequency range.…”

Section: Introductionmentioning

confidence: 99%

Image filtering in structured illumination microscopy using the Lukosz bound

et al. 2013

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Various aspects of image filtering affect the final image quality in Structured Illumination Microscopy, in particular the regularization parameter and type of regularization function, the relative height of the side bands, and the shape of the apodization function. We propose an apodization filter without adjustable parameters based on the application of the Lukosz bound in order to guarantee a non-negative point spread function. Simulations of digital resolution charts and experimental data of chromatin structures and of actin filaments show artefact free reconstructions for a wide range of filter parameters. In general, a trade-off is observed between sharpness and noise suppression.

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Application of second generation wavelets to blind spherical deconvolution

Vareschi

2014

Journal of Multivariate Analysis

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We adress the problem of spherical deconvolution in a non parametric statistical framework, where both the signal and the operator kernel are subject to error measurements. After a preliminary treatment of the kernel, we apply a thresholding procedure to the signal in a second generation wavelet basis. Under standard assumptions on the kernel, we study the theoritical performance of the resulting algorithm in terms of L p losses (p ≥ 1) on Besov spaces on the sphere. We hereby extend the application of second generation spherical wavelets to the blind deconvolution framework [16]. The procedure is furthermore adaptive with regard both to the target function sparsity and smoothness, and the kernel blurring effect. We end with the study of a concrete example, putting into evidence the improvement of our procedure on the recent blockwise-SVD algorithm [6].

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Parametric Blind Deconvolution for Confocal Laser Scanning Microscopy

Cited by 19 publications

References 102 publications

Blind deconvolution for diffraction-limited fluorescence microscopy

Blind deconvolution for diffraction-limited fluorescence microscopy

Image filtering in structured illumination microscopy using the Lukosz bound

Application of second generation wavelets to blind spherical deconvolution

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