Computational confocal tomography for simultaneous reconstruction of objects, occlusions, and aberrations

Dillon, Keith; Fainman, Yeshaiahu

doi:10.1364/ao.49.002529

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…The matrices C and E describe the ray propagation between points in the scene and points on the detector. 6 These matrices are generated in advance using the known view geometry. The log and exponential are taken element-wise over vectors.…”

Section: Methodsmentioning

confidence: 99%

“…Within this framework, constraints can be employed to enforce the physical properties of optics with opacity, and regularization can be employed to address ill-posedness and missing information. 6 The problem remains challenging, however, as constrained optimization generally requires second-order optimization methods and significantly more computational resources. 7 Further, regularization implicitly assumes a very simple statistical model for the unknown information, and the model implied by standard regularization techniques may fit the particular problem poorly.…”

Section: Introductionmentioning

confidence: 99%

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Model-based machine learning for computational reconstruction of opacity and missing information

Dillon

2023

Applications of Machine Learning 2023

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Model-based machine learning methods incorporate domain knowledge from the physical forward model of an inverse problem to reduce the need for training data. In this research, we show how this can be used to address challenging limitations such as occlusion. We combine a convolutional neural network with a novel computational reconstruction method that combines source and attenuation distributions in order to model occlusion. We demonstrate the ability to quickly learn to address reconstruction artifacts and opacity, forming a significantly improved final image of the scene based on as little as a single training image. The algorithm can be implemented efficiently and scaled to large problem sizes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-based machine learning for computational reconstruction of opacity and missing information

Dillon

2023

Applications of Machine Learning 2023

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“…More accurate formulations are based on a so-called cone beam, describing diverging rays. Analogous systems have also been demonstrated with light at optical and infrared frequencies [8,[24][25][26]36]. The optical case relies on the Eikonal approximation [2], which models the high frequency limit where light rays behave as x-rays.…”

Section: Computed Tomographymentioning

confidence: 99%

Imaging with Rays: Microscopy, Medical Imaging, and Computer Vision

Dillon,

Fainman

2014

Preprint

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In this paper we broadly consider techniques which utilize projections on rays for data collection, with particular emphasis on optical techniques. We formulate a variety of imaging techniques as either special cases or extensions of tomographic reconstruction. We then consider how the techniques must be extended to describe objects containing occlusion, as with a self-occluding opaque object. We formulate the reconstruction problem as a regularized nonlinear optimization problem to simultaneously solve for object brightness and attenuation, where the attenuation can become infinite. We demonstrate various simulated examples for imaging opaque objects, including sparse point sources, a conventional multiview reconstruction technique, and a superresolving technique which exploits occlusion to resolve an image.

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“…The main difference with conventional optical phase tomography [11–16] is that the measurement is done with a focused beam scanned in three dimensions rather than with a collimated beam scanned in angle. Dillon and Fainman [17] proposed this concept in 2010 and presented experimental results with an absorptive object (fiber and absorbing stripes on a reflecting surface) [18]. In the DCM, as not only the intensity but also the phase is recorded, we can quantitatively reconstruct the refractive index distribution.…”

Section: Tomographic Interpretation Of the Datamentioning

confidence: 99%

Multiple contrast metrics from the measurements of a digital confocal microscope

Goy

Unser

2013

Biomed. Opt. Express

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We describe various methods to process the data collected with a digital confocal microscope (DCM) in order to get more information than what we could get from a conventional confocal system. Different metrics can be extracted from the data collected with the DCM in order to produce images that reveal different features of the sample. The integrated phase of the scattered field allows for the three-dimensional reconstruction of the refractive index distribution. In a similar way, the integration of the field intensity yields the absorption coefficient distribution. The deflection of the digitally reconstructed focus reveals the sample-induced aberrations and the RMS width of the focus gives an indication on the local scattering coefficient. Finally, in addition to the conventional confocal metric, which consists in integrating the intensity within the pinhole, the DCM allows for the measurement of the phase within the pinhole. This metrics is close to the whole-field integrated phase and thus gives a qualitative image of the refractive index distribution.

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Computational confocal tomography for simultaneous reconstruction of objects, occlusions, and aberrations

Cited by 5 publications

References 13 publications

Model-based machine learning for computational reconstruction of opacity and missing information

Model-based machine learning for computational reconstruction of opacity and missing information

Imaging with Rays: Microscopy, Medical Imaging, and Computer Vision

Multiple contrast metrics from the measurements of a digital confocal microscope

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