GlobalBioIm: A Unifying Computational Framework for Solving Inverse Problems

Unser, Michaël; Soubies, Emmanuel; Soulez, Ferréol; McCann, Michael T.; Donati, Laurène

doi:10.1364/cosi.2017.ctu1b.1

Cited by 22 publications

(17 citation statements)

References 5 publications

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“…For more information on the modalities we have discussed, see the references listed at the end of each example. For a MATLAB software library based on this methodology, see [23].…”

Section: Further Readingmentioning

confidence: 99%

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

McCann

Unser

2019

FNT in Signal Processing

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“…For more information on the modalities we have discussed, see the references listed at the end of each example. For a MATLAB software library based on this methodology, see [23].…”

Section: Further Readingmentioning

confidence: 99%

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

McCann

Unser

2019

FNT in Signal Processing

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“…The minimization problem (17) is solved with a limited-memory quasi-Newton method with bound constraints, VMLM-B [43]. We use the version implemented in the GlobalBioIm framework [44]. Solving (17) implies evaluating the gradient of the data fidelity term.…”

Section: Multi-z Reconstructionsmentioning

confidence: 99%

Reconstruction of in-line holograms: combining model-based and regularized inversion

Berdeu¹,

Flasseur²,

Méès³

et al. 2019

Opt. Express

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In-line digital holography is a simple yet powerful tool to image absorbing and/or phase objects. Nevertheless, the loss of the phase of the complex wavefront on the sensor can be critical in the reconstruction process. The simplicity of the setup must thus be counterbalanced by dedicated reconstruction algorithms, such as inverse approaches, in order to retrieve the object from its hologram. In the case of simple objects for which the diffraction pattern produced in the hologram plane can be modeled using few parameters, a model fitting algorithm is very effective. However, such an approach fails to reconstruct objects with more complex shapes, and an image reconstruction technique is then needed. The improved flexibility of these methods comes at the cost of a possible loss of reconstruction accuracy. In this work, we combine the two approaches (model fitting and regularized reconstruction) to benefit from their respective advantages. The sample to be reconstructed is modeled as the sum of simple parameterized objects and a complex-valued pixelated transmittance plane. These two components jointly scatter the incident illumination, and the resulting interferences contribute to the intensity on the sensor. The proposed hologram reconstruction algorithm is based on alternating a model fitting step and a regularized inversion step. We apply this algorithm in the context of fluid mechanics, where holograms of evaporating droplets are analyzed. In these holograms, the high contrast fringes produced by each droplet tend to mask the diffraction pattern produced by the surrounding vapor wake. With our method, the droplet and the vapor wake can be jointly reconstructed.

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“…Operations (14), (15), (18) and (19) are identical to Lines 4, 5, 8 and 9 in the standard ADMM algorithm, respectively. As currently stated, the proposed algorithm necessitates two steps more than the standard ADMM.…”

Section: Inner-loop-free Admm (Proposed)mentioning

confidence: 99%

“…Moreover, the alternating scheme decouples the physical aspects of the problem from the imposition of prior constraints on the signal, which permits a particularly modular implementation [15]. Yet, the standard ADMM scheme still relies on inner conjugate-gradient (CG) loops to solve the linear step in its minimization procedure.…”

Section: Introductionmentioning

confidence: 99%

Inner-Loop-Free Admm For Cryo-Em

Donati

Soubies

Unser

2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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Thanks to recent advances in signal processing, the interest for fast 1-regularized reconstruction algorithms in cryo-electron microscopy (cryo-EM) has intensified. The approaches based on the alternating-direction of multipliers method (ADMM) are particularly well-suited due to the prime convergence speed and flexibility of use of this algorithm. Yet, the standard ADMM scheme still relies on a nested conjugate gradient (CG) to solve the linear step in its alternating-minimization procedure, which can be costly when handling large-scale problems. In this work, we present an innerloop-free ADMM algorithm for 3D reconstruction in cryo-EM. By using an appropriate splitting scheme, we are able to avoid the use of CG for solving the linear step. This leads to a substantial increase in algorithmic speed, as demonstrated by our experiments.

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GlobalBioIm: A Unifying Computational Framework for Solving Inverse Problems

Cited by 22 publications

References 5 publications

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Reconstruction of in-line holograms: combining model-based and regularized inversion

Inner-Loop-Free Admm For Cryo-Em

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