Rendering near-field speckle statistics in scattering media

Bar, Chen; Gkioulekas, Ioannis; Levin, Anat

doi:10.1145/3414685.3417813

Cited by 12 publications

(12 citation statements)

References 67 publications

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“…We compare the covariance computed using [19], with the one computed from the single-scattering approximation in Eq. (20). For ∆ = 0, we have that î1 = î2 , v1 = v2 , and the correlation reduces to the intensity of the scattered light.…”

Section: Far-field Single-scattering Covariancementioning

confidence: 98%

“…Computing expected speckle correlations makes it possible to adapt incident wavefronts for one point to focus at a neighboring point, without the need for a new guide star. It can also help understand over what ranges this refocusing is possible [13], [14], [20].…”

Section: Speckle Correlation Applicationsmentioning

confidence: 99%

“…Accuracy of the single scattering approximation: We compare the single scattering approximation against the full In each case, we compare on the left the correlation produced with the full simulator of [19], and on the right our single scattering approximation of Eq. (20). The comparison includes materials of 3 different thicknesses, corresponding to optical depths 1, 5 and 30.…”

Section: Far-field Single-scattering Covariancementioning

confidence: 99%

“…This adaptation usually involves small tilting and shifting of the wavefront correction, hence the name tilt-shift memory effect. We refer the reader to [13], [14], [20] for details.…”

Section: Tilt-shift Memory Effectmentioning

confidence: 99%

“…The large number of applications have motivated the creation of simulation tools and mathematical models for analyzing statistical properties of speckle statistics, without the need for painstaking lab measurements [15]. In particular, simulation tools include numerical wave-equation solvers [16], [17], [18], as well as Monte Carlo rendering algorithms [19], [20]. These simulation tools produce physically-accurate results, but they do not lend themselves to mathematical analysis, due to the complexity of the underlying equations that are being simulated.…”

Section: Introductionmentioning

confidence: 99%

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Single scattering modeling of speckle correlation

Bar

Alterman

Gkioulekas

et al. 2021

2021 IEEE International Conference on Computational Photography (ICCP)

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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.Index Terms-Scattering, Speckle statistics. ! îx,y = (0, 0) • (0, 0.2) • (0, 0.4) • (0, 0.6) •

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Section: Far-field Single-scattering Covariancementioning

confidence: 98%

Section: Speckle Correlation Applicationsmentioning

confidence: 99%

Section: Far-field Single-scattering Covariancementioning

confidence: 99%

“…This adaptation usually involves small tilting and shifting of the wavefront correction, hence the name tilt-shift memory effect. We refer the reader to [13], [14], [20] for details.…”

Section: Tilt-shift Memory Effectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Single scattering modeling of speckle correlation

Bar

Alterman

Gkioulekas

et al. 2021

2021 IEEE International Conference on Computational Photography (ICCP)

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An oblique-incidence statistical model for laser speckle

Cui,

Li,

Cong

et al. 2024

Optics Communications

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Imaging with Local Speckle Intensity Correlations: Theory and Practice

et al. 2021

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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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Rendering near-field speckle statistics in scattering media

Cited by 12 publications

References 67 publications

Single scattering modeling of speckle correlation

Single scattering modeling of speckle correlation

An oblique-incidence statistical model for laser speckle

Imaging with Local Speckle Intensity Correlations: Theory and Practice

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