Speckle pattern

Abstract: A speckle pattern formed in polarized monochromatic light may be regarded as resulting from a classical random walk in the complex plane. The resulting irradiance fluctuations obey negative exponential statistics, with ratio of standard deviation to mean (i.e., contrast) of unity. Reduction of this contrast, or smoothing of the speckle, requires diversity in polarization, space, frequency, or time. Addition of M uncorrelated speckle patterns on an intensity basis can reduce the contrast by 1/√M. However, addition of speckle patterns on a complex amplitude basis provides no reduction of contrast. The distribution of scale sizes in a speckle pattern (i.e., the Wiener spectrum) is investigated from a physical point of view.

Abstract: Digital particle image velocimetry (DPIV) is the digital counterpart of conventional laser speckle velocitmetry (LSV) and particle image velocimetry (PIV) techniques. In this novel, two-dimensional technique, digitally recorded video images are analyzed computationally, removing both the photographic and opto-mechanical processing steps inherent to PIV and LSV. The directional ambiguity generally associated with PIV and LSV is resolved by implementing local spatial cross-correlations between two sequential single-exposed particle images. The images are recorded at video rate (30 Hz or slower) which currently limits the application of the technique to low speed flows until digital, high resolution video systems with higher framing rates become more economically feasible. Sequential imaging makes it possible to study unsteady phenomena like the temporal evolution of a vortex ring described in this paper. The spatial velocity measurements are compared with data obtained by direct measurement of the separation of individual particle pairs. Recovered velocity data are used to compute the spatial and temporal vorticity distribution and the circulation of the vortex ring.

Abstract: Overview of Polarimetric Radar Imaging Brief History of Polarimetric Radar Imaging SAR Image Formation: Summary Airborne and Space-Borne PolSAR Systems Description of the Remaining Chapters Electromagnetic Vector Wave and Polarization Descriptors Monochromatic Electromagnetic Plane Wave Polarization Ellipse Jones Vector Stokes Vector Wave Covariance Matrix Electromagnetic Vector Scattering Operators Polarimetric Back Scattering Sinclair S Matrix Scattering Target Vectors k and Omega Polarimetric Coherency T and Covariance C Matrices Polarimetric Mueller M and Kennaugh K Matrices Change of Polarimetric Basis Target Polarimetric Characterization PolSAR Speckle Statistics Fundamental Property of Speckle in SAR Images Speckle Statistics for Multilook-Processed SAR Images Texture Model and K Distribution Effect of Speckle Spatial Correlation Polarimetric and Interferometric SAR Speckle Statistics Phase Difference Distributions of Single-Look and Multilook PolSAR Data Multilook Product Distribution Joint Distribution of Multilook Si2 and Sj2 Multilook Intensity and Amplitude Ratio Distributions Verifications of Multilook PDFs K Distribution for Multilook Polarimetric Data Summary Appendices PolSAR Speckle Filtering Introduction to Speckle Filtering of SAR Imagery Filtering of Single Polarization SAR Data Review of Multipolarization Speckle Filtering Algorithms PolSAR Speckle Filtering Scattering Model-Based PolSAR Speckle Filter Introduction to the Polarimetric Target Decomposition Concept Introduction Dichotomy of the Kennaugh Matrix K Eigenvector-Based Decompositions Model-Based Decompositions Coherent Decompositions The H/A/a Polarimetric Decomposition Theorem Introduction Pure Target Case Probabilistic Model for Random Media Scattering Roll Invariance Property Polarimetric Scattering a Parameter Polarimetric Scattering Entropy (H) Polarimetric Scattering Anisotropy (A) Three-Dimensional H/A/a Classification Space New Eigenvalue-Based Parameters Speckle Filtering Effects on H/A/a PolSAR Terrain and Land-Use Classification Introduction Maximum Likelihood Classifier Based on Complex Gaussian Distribution Complex Wishart Classifier for Multilook PolSAR Data Characteristics of Wishart Distance Measure Supervised Classification Using Wishart Distance Measure Unsupervised Classification Based on Scattering Mechanisms and Wishart Classifier Scattering Model-Based Unsupervised Classification Quantitative Comparison of Classification Capability: Fully PolSAR versus Dual- and Single-Polarization SAR Pol-InSAR Forest Mapping and Classification Introduction Pol-InSAR Scattering Descriptors Forest Mapping and Forest Classification Appendix Selected PolSAR Applications Polarimetric Signature Analysis of Manmade Structures Polarization Orientation Angle Estimation and Applications Ocean Surface Remote Sensing with PolSAR Ionosphere Faraday Rotation Estimation PolSAR Interferometry for Forest Height Estimation Nonstationary Natural Media Analysis from PolSAR Data Using a Two-Dimensional Time-Frequency Approach Appendix A: Eigen Characteristics of Hermitian Matrix Appendix B: PolSARpro Software: The Polariemtric SAR Data Processing and Educational Toolbox Index

Abstract: This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee (1980, 1981, 1986) and Frost (1982) filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.

Abstract: Digital imaging techniques are utilized as a measure of surface displacement components in laser speckle metrology. An image scanner which is interfaced to a computer records and stores in memory the laser speckle patterns of an object in a reference and deformed configuration. Subsets of the deformed images are numerically correlated with the references as a measure of surface displacements. Discrete values are determined around a closed contour for plane problems which then become input into a boundary integral equation method in order to calculate surface traction in the contour. Stresses are then calculated within this boundary. The solution procedure is illustrated by a numerical example of a case of uniform tension.