The autocorrelation function of polychromatic laser speckle patterns near the image plane

1983 ◽  
Vol 15 (6) ◽  
pp. 507-512 ◽  
Author(s):  
N. Nagamatsu ◽  
K. Nakagawa ◽  
T. Asakura ◽  
K. Morishita
Keyword(s):  
Autocorrelation Function ◽  
Image Plane ◽  
Laser Speckle ◽  
Speckle Patterns

Comparison of Surface Roughness Measurement in Dichromatic Speckle Patterns with Autocorrelation Method

2011 ◽  
Vol 189-193 ◽  
pp. 680-683 ◽  
Author(s):  
Zong Heng Yuan ◽  
Yan Hua Zhang ◽  
Jian Wei Zhang ◽  
Ye Fan Ge
Keyword(s):  
Surface Roughness ◽  
Autocorrelation Function ◽  
Correlation Length ◽  
High Reliability ◽  
Laser Speckle ◽  
Roughness Measurement ◽  
Elongation Ratio ◽  
Surface Roughness Measurement ◽  
Speckle Patterns ◽  
Autocorrelation Method

Applying autocorrelation method to process laser speckle patterns, the relation between surface roughness and speckle elongation and correlation length of autocorrelation function can be obtained, and the measured surface roughness can be achieved based on this relation. One-dimension autocorrelation and two-dimension autocorrelation function are used, Moreover, surface roughness is evaluated by speckle elongation and correlation length of autocorrelation function. Aspect ratio of speckle granular represents speckle elongation ratio, which eliminates effects of speckle granular average size on measurement results compared to other methods using before. It has high reliability and efficiency in surface roughness measurement evaluation.

Image Plane Detection Using Laser Speckle Patterns

1973 ◽  
Vol 12 (4) ◽  
pp. 881 ◽  
Author(s):  
T. Sawatari ◽  
A. C. Elek
Keyword(s):  
Image Plane ◽  
Laser Speckle ◽  
Speckle Patterns

scholarly journals Fourier transforms for fast and quantitative Laser Speckle Imaging

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
J. Buijs ◽  
J. van der Gucht ◽  
J. Sprakel
Keyword(s):  
Quantitative Data ◽  
Fourier Transforms ◽  
Imaging Technique ◽  
Laser Speckle ◽  
Frequency Range ◽  
Laser Speckle Imaging ◽  
Speckle Imaging ◽  
New Approach ◽  
Speckle Patterns ◽  
New Processing

Abstract Laser speckle imaging is a powerful imaging technique that visualizes microscopic motion within turbid materials. At current two methods are widely used to analyze speckle data: one is fast but qualitative, the other quantitative but computationally expensive. We have developed a new processing algorithm based on the fast Fourier transform, which converts raw speckle patterns into maps of microscopic motion and is both fast and quantitative, providing a dynamnic spectrum of the material over a frequency range spanning several decades. In this article we show how to apply this algorithm and how to measure a diffusion coefficient with it. We show that this method is quantitative and several orders of magnitude faster than the existing quantitative method. Finally we harness the potential of this new approach by constructing a portable laser speckle imaging setup that performs quantitative data processing in real-time on a tablet.

Contrast of white light speckle patterns at a defocused image plane

1979 ◽  
Vol 18 (22) ◽  
pp. 3725_1 ◽  
Author(s):  
K. Nakagawa ◽  
T. Asakura
Keyword(s):  
White Light ◽  
Image Plane ◽  
Speckle Patterns ◽  
Defocused Image

An artifact-metrics which utilizes laser speckle patterns for plastic ID card surface

2010 ◽  
Author(s):  
Manabu Yamakoshi ◽  
Xiaoying Rong ◽  
Tsutomu Matsumoto
Keyword(s):  
Laser Speckle ◽  
Speckle Patterns

Image synthesis from nonimaged laser-speckle patterns

1987 ◽  
Vol 12 (11) ◽  
pp. 858 ◽  
Author(s):  
Paul S. Idell ◽  
J. R. Fienup ◽  
Ron S. Goodman
Keyword(s):  
Image Synthesis ◽  
Laser Speckle ◽  
Speckle Patterns
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