Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Laser speckle imaging with an active noise reduction scheme

Open Access Open Access

Abstract

We present an optical scheme to actively suppress statistical noise in Laser Speckle Imaging (LSI). This is achieved by illuminating the object surface through a diffuser. Slow rotation of the diffuser leads to statistically independent surface speckles on time scales that can be selected by the rotation speed. Active suppression of statistical noise is achieved by accumulating data over time. We present experimental data on speckle contrast and noise for a dynamically homogenous and a heterogeneous object made from Teflon. We show experimentally that for our scheme spatial and temporal averaging provide the same statistical weight to reduce the noise in LSI: The standard deviation of the speckle contrast value scales with the effective number N of independent speckle as 1/√N.

©2005 Optical Society of America

Full Article  |  PDF Article
More Like This
Dynamic laser speckle imaging of cerebral blood flow

P. Zakharov, A.C. Völker, M.T. Wyss, F. Haiss, N. Calcinaghi, C. Zunzunegui, A. Buck, F. Scheffold, and B. Weber
Opt. Express 17(16) 13904-13917 (2009)

Noise in laser speckle correlation and imaging techniques

S. E. Skipetrov, J. Peuser, R. Cerbino, P. Zakharov, B. Weber, and F. Scheffold
Opt. Express 18(14) 14519-14534 (2010)

Temporal statistical analysis of laser speckle images and its application to retinal blood-flow imaging

Haiying Cheng, Yumei Yan, and Timothy Q. Duong
Opt. Express 16(14) 10214-10219 (2008)

Cited By

Optica participates in Crossref's Cited-By Linking service. Citing articles from Optica Publishing Group journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (4)

Fig. 1.
Fig. 1. Experimental setup: A laser beam (785 nm) is incident on ground glass mounted on a motor (rotation velocity one rph). Light passing the ground glass is moderately divergent and illuminates the sample surface as an expanded light spot. The illuminated surface is imaged via a beam-splitter by the camera objective onto the CCD chip of the digital camera. Right: Heterogeneous sample obtained by milling a cylindrical void (diameter D0 = 3 mm) into a solid block of Teflon. The inclusion is filled with an aqueous suspension of 710 nm polystyrene latex spheres that matches the optical properties (l*=250±30 μm) and creates dynamic contrast. A layer of thickness d = 0.45 mm separates the inclusion from the imaging surface.
Fig. 2.
Fig. 2. Characteristics of the laser speckle imaging setup obtained from a rigid sample (Teflon block) for different LASCA square sizes. a) mean speckle contrast K as a function of the speckle size (in units of the pixel size rP ). b) Standard deviation of the contrast σK as a function of rS . To achieve approximately the same intensity for all apertures the exposure time was varied between 200 and 750 ms.
Fig. 3.
Fig. 3. Three dimensional plot of a laser speckle contrast image of a liquid inclusion in a solid block of Teflon (scale inverted). a) LASCA image using 8×8 square size, image resolution 80×60 pixel averaged over 500 individual measurement. b) Same sample using the active noise reduction. c) Full resolutions 640×480 pixel with active noise reduction scheme.
Fig. 4.
Fig. 4. Left: Noise in speckle contrast, expressed by the standard deviation sk, as a function of the number of frames taken, different square sizes are displayed (where #/pixel is the number of pixels in the corresponding square size). b) σK scaling as a function of “effective pixels” N. It is shown that spatial and temporal averaging give identical results.

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

K = β I 1 [ i = 1 N ( I i I ) 2 N 1 ] 1 2 .
K = β 0 T 2 ( 1 t T ) C ( τ ) T = β e 2 x 1 + 2 x 2 x 2 with x = T τ c .
r s = 2 l k 0 q = 4 × f # × l k 0 f .
Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.