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  1. 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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  2. 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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  3. Coherent images of scattering materials, such as biological tissue, typically exhibit high-frequency intensity fluctuations known as speckle. These seemingly noise-like speckle patterns have strong statistical correlation properties that have been successfully utilized by computational imaging systems in different application areas. Unfortunately, these properties are not well-understood, in part due to the difficulty of simulating physically-accurate speckle patterns. In this work, we propose a new model for speckle statistics based on a single scattering approximation, that is, the assumption that all light contributing to speckle correlation has scattered only once. Even though single-scattering models have been used in computer vision and graphics to approximate intensity images due to scattering, such models usually hold only for very optically thin materials, where light indeed does not scatter more than once. In contrast, we show that the single-scattering model for speckle correlation remains accurate for much thicker materials. We evaluate the accuracy of the single-scattering correlation model through exhaustive comparisons against an exact speckle correlation simulator. We additionally demonstrate the model's accuracy through comparisons with real lab measurements. We show, that for many practical application settings, predictions from the single-scattering model are more accurate than those from other approximate models popular in optics, such as the diffusion and Fokker-Planck models. We show how to use the single-scattering model to derive closed-form expressions for speckle correlation, and how these expressions can facilitate the study of statistical speckle properties. In particular, we demonstrate that these expressions provide simple explanations for previously reported speckle properties, and lead to the discovery of new ones. Finally, we discuss potential applications for future computational imaging systems. 
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  4. Boppart, Stephen A. ; Wojtkowski, Maciej ; Oh, Wang-Yuhl (Ed.)
  5. null (Ed.)