Modeling and removing depth variant blur in 3D fluorescence microscopy

Hadj, Saïma Ben; Blanc‐Féraud, Laure

doi:10.1109/icassp.2012.6287977

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Gaussian noise with standard deviation σ > 0, so that the observed volume is related to x through, y = Hx + b, (77) with vector b ∈ R N accounting for the noise. The goal is to solve the inverse problem of estimating x given y and H. Depth-variant blurs are commonly encountered in 3D microscopy [39,45,46,64], due to optical aberrations. They are particular cases of spatially-variant blurs [12,55].…”

Section: Application To 3d Image Restorationmentioning

confidence: 99%

Block delayed Majorize-Minimize subspace algorithm for large scale image restoration ^*

et al. 2023

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In this work, we propose an asynchronous Majorization-Minimization (MM) algorithm for solving large scale differentiable non-convex optimization problems. The proposed algorithm runs efficient MM memory gradient updates on blocks of coordinates, in a parallel and possibly asynchronous manner. We establish the convergence of the resulting sequence of iterates under mild assumptions. The performance of the algorithm is illustrated on the restoration of 3D images degraded by depth-variant 3D blur, arising in multiphoton microscopy. Significant computational time reduction, scalability and robustness are observed on synthetic data, when compared to state-of-the-art methods. Experiments on the restoration of real acquisitions of a muscle structure illustrate the qualitative performance of our approach and its practical applicability.

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Section: Application To 3d Image Restorationmentioning

confidence: 99%

Block delayed Majorize-Minimize subspace algorithm for large scale image restoration ^*

et al. 2023

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“…Examples of these constrained iterative deconvolution approaches include the Jansson-van Cittert [38] method and the classical maximum likelihood estimator [39]. These non-linear algorithms require more computational resources because of their iterative formulations; therefore, alternative, faster deconvolution techniques have been proposed based on wavelets [40], sparse representations [41], and space-variant blur approximations [42].…”

Section: Image Acquisiɵonmentioning

confidence: 99%

Taking Aim at Moving Targets in Computational Cell Migration

Masuzzo

Troys

Ampè

et al. 2016

Trends in Cell Biology

100

122

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Cell migration is central to the development and maintenance of multicellular organisms. Fundamental understanding of cell migration can, for example, direct novel therapeutic strategies to control invasive tumor cells. However, the study of cell migration yields an overabundance of experimental data that require demanding processing and analysis for results extraction. Computational methods and tools have therefore become essential in the quantification and modeling of cell migration data. We review computational approaches for the key tasks in the quantification of in vitro cell migration: image pre-processing, motion estimation and feature extraction. Moreover, we summarize the current state-of-the-art for in silico modeling of cell migration. Finally, we provide a list of available software tools for cell migration to assist researchers in choosing the most appropriate solution for their needs.

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“…This is computationally demanding, and current algorithms only employ a z-variant PSF. 19,20 The available algorithms also restore the total intensity of the images, but they do not compensate for intensity lost outside of the sample volume. Since a significant part of the intensity is permanently lost due to the fiber, these algorithms would not restore the intensity.…”

mentioning

confidence: 99%

Recovering fluorophore concentration profiles from confocal images near lateral refractive index step changes

2016

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Optical aberrations due to refractive index mismatches occur in various types of microscopy due to refractive differences between the sample and the immersion fluid or within the sample. We study the effects of lateral refractive index differences by fluorescence confocal laser scanning microscopy due to glass or polydimethylsiloxane cuboids and glass cylinders immersed in aqueous fluorescent solution, thereby mimicking realistic imaging situations in the proximity of these materials. The reduction in fluorescence intensity near the embedded objects was found to depend on the geometry and the refractive index difference between the object and the surrounding solution. The observed fluorescence intensity gradients do not reflect the fluorophore concentration in the solution. It is suggested to apply a Gaussian fit or smoothing to the observed fluorescence intensity gradient and use this as a basis to recover the fluorophore concentration in the proximity of the refractive index step change. The method requires that the reference and sample objects have the same geometry and refractive index. The best results were obtained when the sample objects were also used for reference since small differences such as uneven surfaces will result in a different extent of aberration.

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Modeling and removing depth variant blur in 3D fluorescence microscopy

Cited by 5 publications

References 9 publications

Block delayed Majorize-Minimize subspace algorithm for large scale image restoration ^*

Block delayed Majorize-Minimize subspace algorithm for large scale image restoration ^*

Taking Aim at Moving Targets in Computational Cell Migration

Recovering fluorophore concentration profiles from confocal images near lateral refractive index step changes

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Modeling and removing depth variant blur in 3D fluorescence microscopy

Cited by 5 publications

References 9 publications

Block delayed Majorize-Minimize subspace algorithm for large scale image restoration *

Block delayed Majorize-Minimize subspace algorithm for large scale image restoration *

Taking Aim at Moving Targets in Computational Cell Migration

Recovering fluorophore concentration profiles from confocal images near lateral refractive index step changes

Contact Info

Product

Resources

About

Block delayed Majorize-Minimize subspace algorithm for large scale image restoration ^*

Block delayed Majorize-Minimize subspace algorithm for large scale image restoration ^*