Showing papers by "Anat Levin published in 2019"
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TL;DR: A Monte Carlo rendering framework for the physically-accurate simulation of speckle patterns arising from volumetric scattering of coherent waves, and uses it to simulate memory effect observations that were previously only possible through lab measurements, and demonstrate its applicability for computational imaging tasks.
Abstract: We present a Monte Carlo rendering framework for the physically-accurate simulation of speckle patterns arising from volumetric scattering of coherent waves. These noise-like patterns are characterized by strong statistical properties, such as the so-called memory effect, which are at the core of imaging techniques for applications as diverse as tissue imaging, motion tracking, and non-line-of-sight imaging. Our framework allows for these properties to be replicated computationally, in a way that is orders of magnitude more efficient than alternatives based on directly solving the wave equations. At the core of our framework is a path-space formulation for the covariance of speckle patterns arising from a scattering volume, which we derive from first principles. We use this formulation to develop two Monte Carlo rendering algorithms, for computing speckle covariance as well as directly speckle fields. While approaches based on wave equation solvers require knowing the microscopic position of wavelength-sized scatterers, our approach takes as input only bulk parameters describing the statistical distribution of these scatterers inside a volume. We validate the accuracy of our framework by comparing against speckle patterns simulated using wave equation solvers, use it to simulate memory effect observations that were previously only possible through lab measurements, and demonstrate its applicability for computational imaging tasks.
18 citations
TL;DR: In this article, the authors present a Monte Carlo rendering framework for the physically-accurate simulation of speckle patterns arising from volumetric scattering of coherent waves, which are characterized by strong statistical properties such as the so-called memory effect.
Abstract: We present a Monte Carlo rendering framework for the physically-accurate simulation of speckle patterns arising from volumetric scattering of coherent waves. These noise-like patterns are characterized by strong statistical properties, such as the so-called memory effect. These properties are at the core of imaging techniques for applications as diverse as tissue imaging, motion tracking, and non-line-of-sight imaging. Our rendering framework can replicate these properties computationally, in a way that is orders of magnitude more efficient than alternatives based on directly solving the wave equations. At the core of our framework is a path-space formulation for the covariance of speckle patterns arising from a scattering volume, which we derive from first principles. We use this formulation to develop two Monte Carlo rendering algorithms, for computing speckle covariance as well as directly speckle fields. While approaches based on wave equation solvers require knowing the microscopic position of wavelength-sized scatterers, our approach takes as input only bulk parameters describing the statistical distribution of these scatterers inside a volume. We validate the accuracy of our framework by comparing against speckle patterns simulated using wave equation solvers, use it to simulate memory effect observations that were previously only possible through lab measurements, and demonstrate its applicability for computational imaging tasks.
15 citations
23 Jun 2019
TL;DR: In this paper, a physically accurate and computationally efficient Monte Carlo algorithm is proposed to evaluate the complex statistics of speckle fields in scattering media, such as the memory effect, for a large variety of material and imaging parameters.
Abstract: We derive a physically accurate and computationally efficient Monte Carlo algorithm that can be used to evaluate the complex statistics of speckle fields in scattering media. This allows evaluating and studying second-order speckle statistics, such as the memory effect, for a large variety of material and imaging parameters, including turbid materials. This helps bridge the gap between analytical formulas, derived under restrictive assumptions such as diffusion, and empirical lab measurements. It also opens up the possibility for discovering new types of correlation effects, and using those to improve our ability to see through and focus into random media.
24 Jun 2019
TL;DR: Using a new MC simulator, the authors study statistics of speckle fields in scattering media, which allows understanding the Memory Effect limits and using speckles correlations to improve our ability to see through random media.
Abstract: Using a new MC simulator, we study statistics of speckle fields in scattering media. This allows understanding the Memory Effect limits and using speckle correlations to improve our ability to see through random media