Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields

Liu, Renhao; Sun, Yu; Zhu, Jiabei; Tian, Lei; Kamilov, Ulugbek S.

doi:10.1038/s42256-022-00530-3

Cited by 43 publications

(9 citation statements)

References 58 publications

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“…This regularity allows us to efficiently optimize o and ϕ using stochastic gradient descent, starting from random initializations. Related network-based parameterizations of images have been applied successfully to microscopy (39,40), tomographic microscopy (41,42), magnetic resonance imaging (43), and other applications (44)(45)(46)(47)(48)(49)(50).…”

Section: Resultsmentioning

confidence: 99%

NeuWS: Neural wavefront shaping for guidestar-free imaging through static and dynamic scattering media

et al. 2023

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Diffraction-limited optical imaging through scattering media has the potential to transform many applications such as airborne and space-based imaging (through the atmosphere), bioimaging (through skin and human tissue), and fiber-based imaging (through fiber bundles). Existing wavefront shaping methods can image through scattering media and other obscurants by optically correcting wavefront aberrations using high-resolution spatial light modulators—but these methods generally require (i) guidestars, (ii) controlled illumination, (iii) point scanning, and/or (iv) statics scenes and aberrations. We propose neural wavefront shaping (NeuWS), a scanning-free wavefront shaping technique that integrates maximum likelihood estimation, measurement modulation, and neural signal representations to reconstruct diffraction-limited images through strong static and dynamic scattering media without guidestars, sparse targets, controlled illumination, nor specialized image sensors. We experimentally demonstrate guidestar-free, wide field-of-view, high-resolution, diffraction-limited imaging of extended, nonsparse, and static/dynamic scenes captured through static/dynamic aberrations.

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Section: Resultsmentioning

confidence: 99%

NeuWS: Neural wavefront shaping for guidestar-free imaging through static and dynamic scattering media

et al. 2023

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“…Such schemes have also come to be known as "untrained" or "self-supervised" and have been used in a variety of inverse problems. [8][9][10][11][12][13][14][15][16][17] They are beyond the scope of the present paper.…”

Section: Canonical Formulation For Non-invasive 3d Imagingmentioning

confidence: 95%

“…[63][64][65] It is also worthwhile to pinpoint again the self-supervised and neural field-based approaches (mentioned earlier) as they have been applied to the same problem. 14,16 For even stronger scattering, as mentioned earlier, the forward computation generally relies on the beam propagation method instead of inverting the Lippmann-Schwinger equation (24). Using the raw intensity measurement directly, some of the earlier works relied on gradient descent with sparse regularization, 66 which has the benefit that the beam propagation operator is geometrically analogous to a neural network.…”

Section: Pure Dielectric Response Weak Scattering and Negligible Diff...mentioning

confidence: 99%

On the use of deep learning for three-dimensional computational imaging

Barbastathis

Pang²,

Kang³

et al. 2023

Practical Holography XXXVII: Displays, Materials, and Applications

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Deep learning 1 has proven to be an efficient and robust method for many computational imaging systems. 2 The advantages of machine learning, as a rule, are that it is fast-at least in its supervised form after training is complete-and seems exceedingly effective in capturing regularizing priors. Here, we focus the discussion on non-invasive three-dimensional (3D) object reconstruction. One then faces the additional dilemma of choosing the appropriate model of light-matter interaction inside the specimen, i.e. the forward operator. We describe the three stages of approximation that are applicable: weak scattering with weak diffraction (also known as the Radon transform), weak scattering with strong diffraction, and strong scattering. We then overview machine learning approaches for the various models, and glance at the consequences of oversimplifying the forward operator choice.

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“…To solve these reconstruction artifacts, convolutional neural networks have been trained in supervised or unsupervised settings, either using a fully-learned framework or in combination with a physical model [12][13][14][15][16][17]. Different approaches have been considered such as direct inversion, plug-and-play (PnP) priors, or physics-informed neural network (PINN) schemes.…”

Section: Introductionmentioning

confidence: 99%

A CNN-based approach for 3D artefact correction of intensity diffraction tomography images

PIERRÉ,

BRIARD,

DESISSAIRE

et al. 2024

Preprint

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3D reconstructions after tomographic imaging often suffer from elongation artifacts due to the limited-angle acquisitions. The retrieval of the original 3D shape is not an easy task, mainly due to the intrinsic morphological changes that biological objects undergo during their development. Here we present a novel approach for the correction of 3D artifacts after 3D reconstructions of intensity-only tomographic acquisitions. The method relies on a network architecture that combines a volumetric and a 3D finite object approach. The framework was applied on time-lapse images of a mouse preimplantation embryo developing from fertilization to the blastocyst stage, proving the correction of the axial elongation, the recovery of the spherical objects and improved quantitative refractive index values. This work paves the way for novel directions on a generalized non-supervised pipeline suited for different biological samples and imaging conditions.

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Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields

Cited by 43 publications

References 58 publications

NeuWS: Neural wavefront shaping for guidestar-free imaging through static and dynamic scattering media

NeuWS: Neural wavefront shaping for guidestar-free imaging through static and dynamic scattering media

On the use of deep learning for three-dimensional computational imaging

A CNN-based approach for 3D artefact correction of intensity diffraction tomography images

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