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  1. Grid-free Monte Carlo methods based on thewalk on spheres (WoS)algorithm solve fundamental partial differential equations (PDEs) like the Poisson equation without discretizing the problem domain or approximating functions in a finite basis. Such methods hence avoid aliasing in the solution, and evade the many challenges of mesh generation. Yet for problems with complex geometry, practical grid-free methods have been largely limited to basic Dirichlet boundary conditions. We introduce thewalk on stars (WoSt)algorithm, which solves linear elliptic PDEs with arbitrary mixed Neumann and Dirichlet boundary conditions. The key insight is that one can efficiently simulate reflecting Brownian motion (which models Neumann conditions) by replacing the balls used by WoS withstar-shapeddomains. We identify such domains via the closest point on the visibility silhouette, by simply augmenting a standard bounding volume hierarchy with normal information. Overall, WoSt is an easy modification of WoS, and retains the many attractive features of grid-free Monte Carlo methods such as progressive and view-dependent evaluation, trivial parallelization, and sublinear scaling to increasing geometric detail.

     
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    Free, publicly-accessible full text available August 1, 2024
  2. Grid-free Monte Carlo methods such aswalk on spherescan be used to solve elliptic partial differential equations without mesh generation or global solves. However, such methods independently estimate the solution at every point, and hence do not take advantage of the high spatial regularity of solutions to elliptic problems. We propose a fast caching strategy which first estimates solution values and derivatives at randomly sampled points along the boundary of the domain (or a local region of interest). These cached values then provide cheap, output-sensitive evaluation of the solution (or its gradient) at interior points, via a boundary integral formulation. Unlike classic boundary integral methods, our caching scheme introduces zero statistical bias and does not require a dense global solve. Moreover we can handle imperfect geometry (e.g., with self-intersections) and detailed boundary/source terms without repairing or resampling the boundary representation. Overall, our scheme is similar in spirit tovirtual point lightmethods from photorealistic rendering: it suppresses the typical salt-and-pepper noise characteristic of independent Monte Carlo estimates, while still retaining the many advantages of Monte Carlo solvers: progressive evaluation, trivial parallelization, geometric robustness,etc.We validate our approach using test problems from visual and geometric computing.

     
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    Free, publicly-accessible full text available August 1, 2024
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  5. When viewed under coherent illumination, scattering materials such as tissue exhibit highly varying speckle patterns. Despite their noise-like appearance, the temporal and spatial variations of these speckles, resulting from internal tissue dynamics and/or external perturbation of the illumination, carry strong statistical information that is highly valuable for tissue analysis. The full practical applicability of these statistics is still hindered by the difficulty of simulating the speckles and their statistics. This paper proposes an efficient Monte Carlo framework that can efficiently sample physically correct speckles and estimate their covariances. While Monte Carlo algorithms were originally derived for incoherent illumination, our approach simulates complex-valued speckle fields. We compare the statistics of our speckle fields against those produced by an exact numerical wave solver and show a precise agreement, while our simulator is a few orders of magnitude faster and scales to much larger scenes. We also show that the simulator predictions accurately align with existing analytical models and simulation strategies, which currently address various partial settings of the general problem.

     
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  6. We extend iterative phase conjugation algorithms, previously derived for coherent illumination. We show they can be used to focus on incoherent fluorescent sources, and the incoherent emission largely expands penetration depth and convergence speed.

     
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  7. Wavefront shaping correction makes it possible to image fluorescent particles deep inside scattering tissue. This requires determining a correction mask to be placed in both the excitation and emission paths. Standard approaches select correction masks by optimizing various image metrics, a process that requires capturing a prohibitively large number of images. To reduce the acquisition cost, iterative phase conjugation techniques use the observation that the desired correction mask is an eigenvector of the tissue transmission operator. They then determine this eigenvector via optical implementations of the power iteration method, which require capturing orders of magnitude fewer images. Existing iterative phase conjugation techniques assume a linear model for the transmission of light through tissue, and thus only apply to fully coherent imaging systems. We extend such techniques to the incoherent case. The fact that light emitted from different sources sums incoherently violates the linear model and makes linear transmission operators inapplicable. We show that, surprisingly, the nonlinearity due to incoherent summation results in an order-of-magnitude acceleration in the convergence of the phase conjugation iteration. 
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  8. Recent advances in computational imaging have significantly expanded our ability to image through scattering layers such as biological tissues by exploiting the auto-correlation properties of captured speckle intensity patterns. However, most experimental demonstrations of this capability focus on the far-field imaging setting, where obscured light sources are very far from the scattering layer. By contrast, medical imaging applications such as fluorescent imaging operate in the near-field imaging setting, where sources are inside the scattering layer. We provide a theoretical and experimental study of the similarities and differences between the two settings, highlighting the increased challenges posed by the near-field setting. We then draw insights from this analysis to develop a new algorithm for imaging through scattering that is tailored to the near-field setting by taking advantage of unique properties of speckle patterns formed under this setting, such as their local support. We present a theoretical analysis of the advantages of our algorithm and perform real experiments in both far-field and near-field configurations, showing an order-of magnitude expansion in both the range and the density of the obscured patterns that can be recovered. 
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