Far-field diffraction microscopy at λ/10 resolution

Zhang, Ting; Godavarthi, Charankumar; Chaumet, Patrick C.; Maire, Guillaume; Giovannini, Hugues; Talneau, A.; Allain, Marc; Belkebir, Kamal; Sentenac, Anne

doi:10.1364/optica.3.000609

Cited by 51 publications

(37 citation statements)

References 31 publications

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“…Motivated from the DART algorithms in X-ray CT [29,36,39], ODT-DART enables high-resolution and high-precision tomographic reconstructions of 3D RI maps. The current approach is different from a recent work where an approximate knowledge of the sample permittivity was used to enhance the spatial resolution in the reconstruction [40]. For example, ODT-DART does not require the knowledge of the sample permittivity; it only requires the discreteness and the number of the discrete RI values.…”

Section: Discussionmentioning

confidence: 98%

Reconstructions of refractive index tomograms via a discrete algebraic reconstruction technique

Shin¹,

Park²

2017

Opt. Express

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Optical diffraction tomography (ODT) provides three-dimensional refractive index (RI) tomograms of a transparent microscopic object. However, because of the finite numerical aperture of objective lenses, ODT has the limited access to diffracted light and suffers from poor spatial resolution, particularly along the axial direction. To overcome the limitation of the quality of RI tomography, we present an algorithm that accurately reconstructs RI tomography using preliminary information that the RI values of a specimen are discrete and uniform. Through simulations and experiments on various samples, including microspheres, red blood cells, and water droplets, we show that the proposed method can precisely reconstruct RI tomograms of samples with discrete and uniform RI values in the presence of the missing information and noise.

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Section: Discussionmentioning

confidence: 98%

Reconstructions of refractive index tomograms via a discrete algebraic reconstruction technique

Shin¹,

Park²

2017

Opt. Express

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“…Two common regularizers for images include the spatial sparsity-promoting penalty R(x) τ x 1 and total variation (TV) penalty R(x) τ Dx 1 , where D is the discrete gradient operator [16,32,33]. Two common methods for solving optimization problems of form (2) are FISTA [34] and ADMM [35], both of which were successfully applied to the problem of image reconstruction from scattered light data [11,12,14,20].…”

Section: Nonlinear Inverse Problemmentioning

confidence: 99%

“…However, these linear approximations are inaccurate when scattering is strong, which leads to reconstruction artifacts for objects that are large or have high permittivity contrasts [5]. For strongly scattering objects, it is preferable to use nonlinear measurement models that can account for multiple light scattering inside the object [6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Efficient and accurate inversion of multiple scattering with deep learning

2018

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Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography. The reconstruction problem is often formulated as a nonconvex optimization, where a nonlinear measurement model is used to account for multiple scattering and regularization is used to enforce prior constraints on the object. In this paper, we propose a powerful alternative to this optimization-based view of image reconstruction by designing and training a deep convolutional neural network that can invert multiple scattered measurements to produce a high-quality image of the refractive index. Our results on both simulated and experimental datasets show that the proposed approach is substantially faster and achieves higher imaging quality compared to the state-of-the-art methods based on optimization.

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“…CSI alternates between estimating the contrast source and the permittivity. Hybrid methods (HM) [34]- [36] combine IL and CSI, aiming to benefit from each of their advantages. A comprehensive comparison of these three methods can be found in the review paper [35].…”

Section: B Related Workmentioning

confidence: 99%

Accelerated Image Reconstruction for Nonlinear Diffractive Imaging

Mansour

Liu

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The problem of reconstructing an object from the measurements of the light it scatters is common in numerous imaging applications. While the most popular formulations of the problem are based on linearizing the object-light relationship, there is an increased interest in considering nonlinear formulations that can account for multiple light scattering. In this paper, we propose an image reconstruction method, called CISOR, for nonlinear diffractive imaging, based on a nonconvex optimization formulation with total variation (TV) regularization.The nonconvex solver used in CISOR is our new variant of fast iterative shrinkage/thresholding algorithm (FISTA). We provide fast and memory-efficient implementation of the new FISTA variant and prove that it reliably converges for our nonconvex optimization problem. In addition, we systematically compare our method with other state-of-the-art methods on simulated as well as experimentally measured data in both 2D and 3D settings.

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Far-field diffraction microscopy at λ/10 resolution

Cited by 51 publications

References 31 publications

Reconstructions of refractive index tomograms via a discrete algebraic reconstruction technique

Reconstructions of refractive index tomograms via a discrete algebraic reconstruction technique

Efficient and accurate inversion of multiple scattering with deep learning

Accelerated Image Reconstruction for Nonlinear Diffractive Imaging

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