Spatial filtering velocimetry of objective speckles for measuring out-of-plane motion

Abstract

This paper analyzes the dynamics of objective laser speckles as the distance between the object and the observation plane continuously changes. With the purpose of applying optical spatial filtering velocimetry to the speckle dynamics, in order to measure out-of-plane motion in real time, a rotational symmetric spatial filter is designed. The spatial filter converts the speckle dynamics into a photocurrent with a quasi-sinusoidal response to the out-of-plane motion. The spatial filter is here emulated with a CCD camera, and is tested on speckles arising from a real application. The analysis discusses the selectivity of the spatial filter, the nonlinear response between speckle motion and observation distance, and the influence of the distance-dependent speckle size. Experiments with the emulated filters illustrate performance and potential applications of the technology.

Selectivity of spatial filtering velocimetry of objective speckles for measuring out-of-plane motion

“…Such an image pair is divided into 7×9 interrogation areas and the cross-correlation function is applied to each of interrogation area. Based on these vector maps, an average position for the centre of speckle contraction is estimated 12 . We find that throughout the full range of 23.0 mm the average centre of the expansion is located at r c,all = (549.5, 598.3) (units of pixels) with a systematic error of less than 1 pixel, and a standard deviation of 4.5 and 8.2 pixels in vertical and horizontal direction, respectively.…”

“…The random errors are below ±0.5 mm within the entire range. The theoretical plot is described in 12 .…”

“…Generically, the two photocurrents can be balanced without affecting the filter signals by defining an even number of angular sectors in the transmission function, where the phases between adjacent sectors are shifted π mutually 12 . Letting ϕ noting the angular coordinate of the polar coordinate system, the transmission function becomes: Figure 3 shows the transmission function described in Eq.13.…”

A Monte Carlo framework for rendering speckle statistics in scattering media

“…ere exist several imaging techniques that directly leverage secondorder speckle statistics. Example applications include motion tracking (Jacquot and Rastogi 1979;Jakobsen et al 2012;Smith et al 2017), looking around the corner (Batarseh et al 2018;Freund 1990;Katz et al 2012), and seeing through (Bertolo i et al 2012;Katz et al 2014) or focusing through (Mosk et al 2013;Nixon et al 2013;Osnabrugge et al 2017;Vellekoop and Aegerter 2010) tissue and other sca ering layers. Most of these imaging techniques rely on the memory e ect of speckles, a fact that has motivated signi cant research on quantifying this e ect for di erent materials.…”

Rendering near-field speckle statistics in scattering media

“…Speckle statistics have found wide applicability in computational imaging. Example applications include motion tracking [Jacquot and Rastogi 1979;Jakobsen et al 2012;Smith et al 2017], looking around the corner [Batarseh et al 2018;Freund 1990;Katz et al 2012], and seeing through [Abookasis and Rosen 2004;Bertolotti et al 2012;Katz et al 2014;Rosen and Abookasis 2003;Takasaki and Fleischer 2014] or focusing through [Choi et al 2011;Edrei and Scarcelli 2016;Katz et al 2010Katz et al , 2012Lai et al 2015;Nixon et al 2013;Rueckel et al 2006;van Putten et al 2011;Vellekoop and Aegerter 2010;Vellekoop et al 2012Vellekoop and Mosk 2007;Yaqoob et al 2008] tissue and other scattering layers. Most of these imaging techniques rely on the memory effect of speckles, and therefore are based on spatial correlations between speckle images.…”