Speckle noise

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 and Frost 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.

Angular differential imaging is a high-contrast imaging technique that reduces quasistatic speckle noise and facilitates the detection of nearby companions. A sequence of images is acquired with an altitude/azimuth telescope while the instrument field derotator is switched off. This keeps the instrument and telescope optics aligned and allows the field of view to rotate with respect to the instrument. For

Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within the framework of wavelet analysis, which reduces speckle in SAR images while preserving the structural features and textural information of the scene. First, we show that the subband decompositions of logarithmically transformed SAR images are accurately modeled by alpha-stable distributions, a family of heavy-tailed densities. Consequently, we exploit this a priori information by designing a maximum a posteriori (MAP) estimator. We use the alpha-stable model to develop a blind speckle-suppression processor that performs a nonlinear operation on the data and we relate this nonlinearity to the degree of non-Gaussianity of the data. Finally, we compare our proposed method to current state-of-the-art soft thresholding techniques applied on real SAR imagery and we quantify the achieved performance improvement.

Ultrasound imaging systems provide the clinician with non-invasive, low cost, and real-time images that can help them in diagnosis, planning and therapy. However, although the human eye is able to derive the meaningful information from these images, automatic processing is very difficult due to noise and artifacts present in the image. The Speckle Reducing Anisotropic Diffusion filter was recently proposed to adapt the anisotropic diffusion filter to the characteristics of the speckle noise present in the ultrasound images and to facilitate automatic processing of images. We analyze the properties of the numerical scheme associated with this filter, using a semi-explicit scheme. We then extend the filter to a matrix anisotropic diffusion, allowing different levels of filtering across the image contours and in the principal curvature directions. We also show a relation between the local directional variance of the image intensity and the local geometry of the image, which can justify the choice of the gradient and the principal curvature directions as a basis for the diffusion matrix. Finally, different filtering techniques are compared on a 2D synthetic image with two different levels of multiplicative noise and on a 3D synthetic image of a Y-junction, and the new filter is applied on a 3D real ultrasound image of the liver. 1

Speckle filter performance depends strongly on the speckle and scene models used as the basis for filter development. These models implicitly incorporate certain assumptions about speckle, scene, and observed signals. In this study, the multiplicative and the product speckle models, which have been used for the development of most of the well-known filters, are analyzed, and their implicit assumptions with

Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women [45]-[48]. In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis [9], [10], [40]

In synthetic aperture radar (SAR) image processing and analysis, the logarithmic transform is often employed to convert the multiplicative speckle model to an additive noise model. However, this nonlinear operation totally changes the statistics of SAR images. In this communication, we first review the statistical properties of speckle noise in both the intensity and the amplitude formats. Then, we derive the probability density functions, the mean values, and the variances to characterize the log-transformed speckle. Finally we discuss the problems introduced by the logarithmic transform on statistical analysis of SAR images. The statistical models developed in this communication will facilitate subsequent SAR image processing tasks based on the additive noise model.

Multiplicative noise (also known as speckle noise) models are central to the study of coherent imaging systems, such as synthetic aperture radar and sonar, and ultrasound and laser imaging. These models introduce two additional layers of difficulties with respect to the standard Gaussian additive noise scenario: (1) the noise is multiplied by (rather than added to) the original image; (2) the noise is not Gaussian, with Rayleigh and Gamma being commonly used densities. These two features of multiplicative noise models preclude the direct application of most state-of-the-art algorithms, which are designed for solving unconstrained optimization problems where the objective has two terms: a quadratic data term (log-likelihood), reflecting the additive and Gaussian nature of the noise, plus a convex (possibly nonsmooth) regularizer (e.g., a total variation or wavelet-based regularizer/prior). In this paper, we address these difficulties by: (1) converting the multiplicative model into an additive one by taking logarithms, as proposed by some other authors;

Segmentation of retinal layers from OCT images is fundamental to diagnose the progress of retinal diseases. In this study we show that the retinal layers can be automatically and/or interactively located with good accuracy with the aid of local coherence information of the retinal structure. OCT images are processed using the ideas of texture analysis by means of the structure tensor combined with complex diffusion filtering. Experimental results indicate that our proposed novel approach has good performance in speckle noise removal, enhancement and segmentation of the various cellular layers of the retina using the STRATUSOCT TM system.

We present a comparative study between a complex Wavelet Coe cient Shrinkage (WCS) lter and several standard speckle lters that are widely used in the radar imaging community (Lee, Kuan, Frost, Geometric, Kalman, Gamma, etc.). The WCS lter is based on the use of Symmetric Daubechies (SD) wavelets which share the same properties as the real Daubechies wavelets but with an additional symmetry property. The ltering operation is an elliptical soft-thresholding procedure with respect to the principal axes of the 2-D complex wavelet coe cient distributions. Both qualitative and quantitative results (signal to mean square error ratio, equivalent number of looks, edgemap gure of merit) are reported. Tests have been performed using simulated speckle noise as well as real radar images. It is found that the WCS lter performs equally well as the standard lters for low-level noise and slightly outperforms them for higher-level noise.

We present the results of the Gemini Deep Planet Survey, a near-infrared adaptive optics search for giant planets and brown dwarfs around nearby young stars. The observations were obtained with the Altair adaptive optics system at the Gemini North telescope and angular differential imaging was used to suppress the speckle noise of the central star. Detection limits for the 85 stars observed are presented, along with a list of all faint point sources detected around them. Typically, the observations are sensitive to angular separations beyond 0.5 ′′ with 5σ contrast sensitivities in magnitude difference at 1.6 µm of 9.5 at 0.5 ′′ , 12.9 at 1 ′′ , 15.0 at 2 ′′ , and 16.5 at 5 ′′ . For the typical target of the survey, a 100 Myr old K0 star located 22 pc from the Sun, the observations are sensitive enough to detect planets more massive than 2 M Jup with a projected separation in the range 40-200 AU. Depending on the age, spectral type, and distance of the target stars, the detection limit can be as low as ∼1 M Jup . Second epoch observations of 48 stars with candidates (out of 54) have confirmed that all candidates are unrelated background stars. A detailed statistical analysis of the survey results, yielding upper limits on the fractions of stars with giant planet or low mass brown dwarf companions, is presented. Assuming a planet mass distribution dn/dm ∝ m −1.2 and a semi-major axis distribution dn/da ∝ a −1 , the 95% credible upper limits on the fraction of stars with at least one planet of mass 0.5-13 M Jup are 0.28 for the range 10-25 AU, 0.13 for 25-50 AU, and 0.093 for 50-250 AU; this result is weakly dependent on the semi-major axis distribution power-law index. The 95% credible interval for the fraction of stars with at least one brown dwarf companion having a semi-major axis in the range 25-250 AU is 0.019 +0.083 −0.015 , irrespective of any assumption on the mass and semi-major axis distributions. The observations made as part of this survey have resolved the stars HD 14802, HD 166181, and HD 213845 into binaries for the first time.

Image cross-spectra obtained by combining pairs of single look SAR images are utilized in an inversion scheme for extracting the underlying ocean wave spectrum. The reasons for proposing the use of image cross-spectra instead of standard multilook spectra are twofold. First, the image cross-spectra are shown to significantly reduce the speckle noise level while preserving the spectral shape. Second, the image cross-spectra provide information about the wave propagation direction. A closed form expression of the ocean-to-SAR spectral transform and its inversion is formulated for the cross-spectral case. The inversion method is based on computing the full derivative of the nonlinear ocean-to-SAR image cross-spectral transform. The SAR image cross-spectra are shown to yield a SNR typically 16-25 dB higher than the corresponding standard 3 look image spectra. The noise level is typically reduced with a factor of 20 compared to the 3 look spectra and a factor of 60 compared to the single look spectra. The inherent information about the wave propagation direction is utilized in the inversion scheme to resolve the 18Megree ambiguity without using any external information. The method is demonstrated on airborne C-band SAR data.

B-scan ultrasound provides a non-invasive low-cost imaging solution to primary care diagnostics. The inherent speckle noise in the images produced by this technique introduces uncertainty in the representation of their textural characteristics. To cope with the uncertainty, we propose a novel fuzzy feature extraction method to encode local texture. The proposed method extends the Local Binary Pattern (LBP) approach by incorporating fuzzy logic in the representation of local patterns of texture in ultrasound images. Fuzzification allows a Fuzzy Local Binary Pattern (FLBP) to contribute to more than a single bin in the distribution of the LBP values used as a feature vector. The proposed FLBP approach was experimentally evaluated for supervised classification of nodular and normal samples from thyroid ultrasound images. The results validate its effectiveness over LBP and other common feature extraction methods.

We address the denoising of images contaminated with multiplicative noise, e.g. speckle noise. Classical ways to solve such problems are filtering, statistical (Bayesian) methods, variational methods, and methods that convert the multiplicative noise into additive noise (using a logarithmic function), shrinkage of the coefficients of the log-image data in a wavelet basis or in a frame, and transform back the result using an exponential function.

Speckle noise is an inherent nature of ultrasound images, which may have negative effect on image interpretation and diagnostic tasks. In this paper, we propose several multi-scale nonlinear thresholding methods for ultrasound speckle suppression. The wavelet coefficients of the logarithm of image are modeled as the sum of a noise-free component plus an independent noise. Assuming that the noise-free components have some local mixture distribution (MD), and the noise is either Gaussian or Rayleigh, we derive the minimum mean squared error (MMSE) and the averaged maximum a posteriori (MAP) estimators for noise reduction. We use Gaussian and Laplacian MD for each noise-free wavelet coefficient to characterize their heavy-tailed property. Since we estimate the parameters of the MD using the expectation maximization (EM) algorithm and local neighbors, the proposed MD incorporates some information about the intra-scale dependency of the wavelet coefficients. To evaluate our spatially adaptive despeckling methods, we use both real medical ultrasound and synthetically introduced speckle images for speckle suppression. The simulation results show that our method outperforms several recently and the stateof-the-art techniques qualitatively and quantitatively.

Accurate and noninvasive measurement of tissue optical properties can be used for biomedical diagnostics and monitoring of tissue analytes. Noninvasive measurement of tissue optical properties ͑total attenuation and scattering coefficients, optical thickness, etc.͒ can be performed with the optical coherence tomography ͑OCT͒ technique. However, speckle noise substantially deteriorates the accuracy of the measurements with this technique. We studied suppression of speckle noise for accurate measurement of backscattering signal and scattering coefficient with the OCT technique. Our results demonstrate that the precision of measurement of backscattering signals with the OCT technique can be 0.2% for homogeneously scattering media and 0.7% for skin, if spatial averaging of speckle noise is applied. This averaging allows us to achieve the precision of tissue scattering coefficient measurements of approximately Ϯ0.8%. This precision can be further improved by a factor of 2-3, upon optimization of OCT operating parameters.

Increasingly, more importance is placed on the uncertainty information of data being displayed. This paper focuses on techniques for visualizing 3D scalar data sets with corresponding uncertainty information at each point which is also represented as a scalar value. In Djurcilov (in: D. Ebert, J.M. Favre, R. Peikert (Eds.), Data Visualization 2001, Springer, Berlin, 2001), we presented two general methods (inline DVR approach and a post-processing approach) for carrying out this task. The first method involves incorporating the uncertainty information directly into the volume rendering equation. The second method involves post-processing information of volume rendered images to composite uncertainty information.Here, we provide further improvements to those techniques primarily by showing the depth cues for the uncertainty, and also better transfer function selections.

A comprehensive EM300 multibeam echo-sounder dataset acquired from Cook Strait, New Zealand, is used to develop a regional-scale objective characterisation of the seafloor. Sediment samples and highresolution seismic data are used for ground-truthing. SonarScope s software is used to process the data, including signal corrections from sensor bias, specular reflection compensation and speckle noise filtering aiming at attenuating the effects of recording equipment, seafloor topography, and water column. The processing is completed by correlating a quantitative description (the Generic Seafloor Acoustic Backscatter-GSAB model) with the backscatter data. The calibrated Backscattering Strength (BS) is used to provide information on the physical characteristics of the seafloor. The imagery obtained from the BS statistical compensation is used for qualitative interpretation only; it helps characterizing sediment facies variations as well as geological and topographic features such as sediment waves and erosional bedforms, otherwise not recognised with the same level of detail using conventional surveying. The physical BS angular response is a good indicator of the sediment grain size and provides a first-order interpretation of the substrate composition. BS angular response for eight reference areas in the Narrows Basin are selected and parameterised using the GSAB model, and BS angular profiles for gravelly, sandy, and muddy seafloors are used as references for inferring the grain size in the reference areas. We propose to use the calibrated BS at 451 incidence angle (BS 45 ) and the Specular-To-Oblique Contrast (STOC) as main global descriptors of the seafloor type. These two parameters enable global backscatter studies by opposition to compensated imagery whose intensity is not comparable from one zone to the other. The results obtained highlight the interest of BS measurements for seafloor remote sensing in a context of habitat-mapping applications.

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The major obstacle to the direct detection of companions to nearby stars is the overwhelming brightness of the host star. Current instruments employing the combination of adaptive optics (AO) and coronagraphy can typically detect objects within 2 ′′ of the star that are ∼ 10 4−5 times fainter. Correlated speckle noise is one of the biggest obstacles limiting such high-contrast imaging. We have obtained a series of 284 8 s, AO-corrected, coronagraphically occulted H-band images of the star Vega at the 3.63 m AEOS telescope located on Haleakala, Hawaii. This dataset is unique for studying the temporal behavior of speckle noise, and represents the first time such a study on highly corrected coronagraphic AO images has been carried out in a quantitative way. We find the speckle pattern to be highly stable in both position and time in our data. This is due to the fact that the AO system corrects disturbances to the stellar wave front at the level where the instrumental wave front errors dominate. Because of this, we find that our detection limit is not significantly improved simply with increased exposure time alone. However, we are able to improve our dynamic range by 1.5-2 magnitudes through subtraction of static/quasi-static speckles in two rotating frames: the telescope pupil frame and the deformable mirror frame. The highly stable nature of speckles will exist for any program using a combination of coronagraphy and high-order AO, and underscores the importance of calibration of non-common path errors between the wave front sensor and the image plane. Such calibration is critical for high-contrast AO systems and we demonstrate this using empirical data. From our data, we are able to constrain the mass of any purported companion to Vega to be less than ∼ 45M J at 8 AU and less than ∼ 30M J at 16 AU, radii not previously probed at these sensitivities.

Three-dimensional shape recovery from one or multiple observations is a challenging problem of computer vision. In this paper, we present a new Focus Measure for the estimation of a depth map using image focus. This depth map can subsequently be used in techniques and algorithms leading to the recovery of a three-dimensional structure of the object, a requirement of a number of high level vision applications. The proposed Focus Measure has shown robustness in the presence of noise as compared to the earlier Focus Measures. This new Focus Measure is based on an optical transfer function implemented in the Fourier domain. The results of the proposed Focus Measure have shown drastic improvements in estimation of a depth map, with respect to the earlier Focus Measures, in the presence of various types of noise including Gaussian, Shot, and Speckle noises. The results of a range of Focus Measures are compared using root mean square error and correlation metric measures. ᭧

In long adaptive optics corrected exposures, exoplanet detections are currently limited by speckle noise originating from the telescope and instrument optics, and it is expected that such noise will also limit future high-contrast imaging instruments for both ground- and space-based telescopes. Previous theoretical analyses have shown that the time intensity variations of a single speckle follow a modified Rician. It

The paper presents a novel despeckling method, based on Daubechies complex wavelet transform, for medical ultrasound images. Daubechies complex wavelet transform is used due to its approximate shift invariance property and extra information in imaginary plane of complex wavelet domain when compared to real wavelet domain. A wavelet shrinkage factor has been derived to estimate the noise-free wavelet coefficients. The proposed method firstly detects strong edges using imaginary component of complex scaling coefficients and then applies shrinkage on magnitude of complex wavelet coefficients in the wavelet domain at non-edge points. The proposed shrinkage depends on the statistical parameters of complex wavelet coefficients of noisy image which makes it adaptive in nature. Effectiveness of the proposed method is compared on the basis of signal to mean square error (SMSE) and signal to noise ratio (SNR). The experimental results demonstrate that the proposed method outperforms other conventional despeckling methods as well as wavelet based log transformed and non-log transformed methods on test images. Application of the proposed method on real diagnostic ultrasound images has shown a clear improvement over other methods.

In ultrasound (US) imaging, denoising is intended to improve quantitative image analysis techniques. In this paper, a new version of the Non Local (NL) Means filter adapted for US images is proposed. Originally developed for Gaussian noise removal, a Bayesian framework is used to adapt the NL means filter for speckle noise. Experiments were carried out on synthetic data sets with different speckle simulations. Results show that our NL means-based speckle filter outperforms the classical implementation of the NL means filter, as well as two other speckle adapted denoising methods (SRAD and SBF filters).

In this paper,we propose the use of binary partition trees (BPT) to introduce a novel region-based and multi-scale polarimetric SAR (PolSAR) data representation. The BPT structure represents homogeneous regions in the data at different detail levels. The construction process of the BPT is based, firstly, on a region model able to represent the homogeneous areas, and, secondly, on a dissimilarity measure in order to identify similar areas and define the merging sequence. Depending on the final application, a BPT pruning strategy needs to be introduced. In this paper, we focus on the application of BPT PolSAR data representation for speckle noise filtering and data segmentation on the basis of the Gaussian hypothesis, where the average covariance or coherency matrices are considered as a region model. We introduce and quantitatively analyze different dissimilarity measures. In this case, and with the objective to be sensitive to the complete polarimetric information under the Gaussian hypothesis, dissimilarity measures considering the complete covariance or coherency matrices are employed. When confronted to PolSAR speckle filtering, two pruning strategies are detailed and evaluated. As presented, the BPT PolSAR speckle filter defined filters data according to the complete polarimetric information. As shown, this novel filtering approach is able to achieve very strong filtering while preserving the spatial resolution and the polarimetric information. Finally, the BPT representation structure is employed for high spatial resolution image segmentation applied to coastline detection. The analyses detailed in this work are based on simulated, as well as on real PolSAR data acquired by the ESAR system of DLR and the RADARSAT-2 system. Index Terms-Binary partition tree (BPT), polarimetry, segmentation, speckle filtering, synthetic aperture radar.

This article investigates and compiles some of the techniques mostly used in the smoothing or suppression of speckle noise in ultrasound images. With this information, a comparison of all the methods studied is done based on an experiment, using quality metrics to test their performance and show the benefits each one can contribute. To test the methods, a synthetic, noise-free image of a kidney is created and later simulations using Field II program to corrupt it are performed. This way, the smoothing techniques can be compared using numeric metrics, taking the noise-free image as a reference. Since real ultrasound images are already noise corrupted images and real noise-free images do not exist, conventional metrics cannot be used to indicate the quality obtained with filtering. Nevertheless, we propose the use of the tendencies observed in our study in real images.

The presence of speckle noise in the reconstruction process of digital hologram reduces the signal to noise ratio (SNR) in the reconstructed images. In this paper we present wavelet filtering to improve SNR in the reconstructed images. Experimental results are presented.

Due to spatial and temporal variability an effective monitoring system for water resources must consider the use of remote sensing to provide information. Synthetic Aperture Radar (SAR) is useful due to timely data acquisition and sensitivity to surface water and flooded vegetation. The ability to map flooded vegetation is attributed to the double bounce scattering mechanism, often dominant for this target. Dong Ting Lake in China is an ideal site for evaluating SAR data for this application due to annual flooding caused by mountain snow melt causing extensive changes in flooded vegetation. A curvelet-based approach for change detection in SAR imagery works well as it highlights the change and suppresses the speckle noise. This paper addresses the extension of this change detection technique to polarimetric SAR data for monitoring surface water and flooded vegetation. RADARSAT-2 images of Dong Ting Lake demonstrate this curvelet-based change detection technique applied to wetlands although it is applicable to other land covers and for post disaster impact assessment. These tools are important to Digital Earth for map updating and revision.

The useful dynamic range of an image in the diffraction limited regime is usually limited by speckles caused by residual phase errors in the optical system forming the image. The technique of speckle decorrelation involves introducing many independent realizations of additional phase error into a wavefront during one speckle lifetime, changing the instantaneous speckle pattern. A commonly held assumption is that this results in the speckles being 'moved around' at the rate at which the additional phase screens are applied. The intention of this exercise is to smooth the speckles out into a more uniform background distribution during their persistence time, thereby enabling companion detection around bright stars to be photon noise limited rather than speckle-limited. We demonstrate analytically why this does not occur, and confirm this result with numerical simulations. We show that the original speckles must persist, and that the technique of speckle decorrelation merely adds more noise to the original speckle noise, thereby degrading the dynamic range of the image.

In optical coherence tomography (OCT), images are usually formed from the envelope of the measured interference signal. Computation of the absolute magnitude of the signal for measurement of the envelope is a nonlinear process that destroys phase information. This study explores the idea of recording and processing the phase of the OCT interference signal before calculation of the magnitudes for display. Processing the partially coherent OCT signals in the complex domain provides the opportunity to correct phase aberrations responsible for speckle noise in OCT images. We describe an OCT system that incorporates a quadraturedemodulation scheme for accurate recording of the phase and amplitude of OCT signals from single or multiple detectors. A speckle-reduction technique that works in the complex domain, called the zeroadjustment procedure (ZAP), is investigated as an example of complex-domain processing. After demonstrating its speckle-correction properties mathematically and in numerical simulations, we apply ZAP to OCT images of living skin. The results show that ZAP reduces speckle contrast in regions where scatterer density is high and expands the range of gray values in the image. However, as presently implemented, ZAP tends to blur sharp boundaries between image features.

Use of multiple look directions, that is, compound imaging, has been shown previously to increase detection of specular reflectors and averaging of speckle noise in gray-scale images, often at the expense of spatial resolution and other misregistration errors. In color flow imaging, additional view angles can fill in vessels missed due to Doppler angle dropout and increase quantitative and visual Doppler accuracy by triangulation or a simple peak-frequency-shift combination algorithm. Image registration and unwarping throughout multiple three-dimensional (3D) volume sets should correct for many refraction artifacts, motion between and during compounded image sets and even, possibly, positioning errors between image sets, acquired months apart, to display growth of abnormaiities. The registration described here does not provide sufficient accuracy for formation of enhanced coherent apertures, but shows promise in some cases to provide superior compound images and possibly comparisons of current and prior studies. In this study, the breast is stabilized by mild compression between a flat plate and a scanning membrane. Registration and unwarping is performed retrospectively on two separate volumetric data sets by defining pairs of corresponding points and, in some cases, line and plane segments. Three-dimensional lhtear affine transforms are performed using identified points, lines and planes. 3D nonlinear warped transforms are also possible given adequate numbers of identifiable points. More than two data sets are registered by selecting one as the standard, and registering the remainder to match. The most appropriate algorithm, such as averaging or maximum amplitude, may be used to combine the data sets for display. Signiiicant success has been achieved in compound display of a test object and of the breast in viva, even when there was relative motion or warping between image sets. In pulse-echo imaging, homologous feature registration for compounding appears to have advantages over mechanically registered compounding methods previously employed in the breast and signiiicaut increases in lesion and structural conspicuity are noted due to a reduction in speckle noise. The improvements from compounding in 3D, surface-rendered Doppler imaging of vasculature are striking.

We demonstrate the versatility of a dual imaging polarimeter working in tandem with a Lyot coronagraph and Adaptive Optics to suppress the highly static speckle noise pattern-the greatest hindrance to ground-based direct imaging of planets and disks around nearby stars. Using a double difference technique with the polarimetric data, we quantify the level of speckle suppression, and hence improved sensitivity, by placing an ensemble of artificial faint companions into real data, with given total brightness and polarization. For highly polarized sources within 0.5 ′′ , we show that we achieve 3 to 4 magnitudes greater sensitivity through polarimetric speckle suppression than simply using a coronagraph coupled to a high-order Adaptive Optics system. Using such a polarimeter with a classical Lyot coronagraph at the 3.63-m AEOS telescope, we have obtained a 6.5σ detection in the H-band of the 76 AU diameter circumstellar debris disk around the star HR 4796A. Our data represent the first definitive ground-based near-IR polarimetric image of the HR 4796A debris disk and clearly show the two outer ansae of the disk, evident in Hubble Space Telescope NICMOS/STIS imaging. Comparing our peak linearly polarized flux with the total intensity in the lobes as observed by NICMOS, we derive a lower limit to the fractional linear polarization of > 29% caused by dust grains in the disk. In addition, we fit simple morphological models of optically thin disks to our data allowing us to constrain the dust disk scale height (2.5 +5.0 −1.3 AU) and scattering asymmetry parameter (g=<cos θ>= 0.20 +.07 −.10 ). These values are consistent with several lines of evidence suggesting that the HR 4796A disk is dominated by a micron-sized dust population, and are indeed typical of disks in transition between those surrounding the Herbig Ae stars to those associated with Vega-like stars.

In this paper, the feasibility of using optical methods to detect impact damage in thin laminates is investigated and discussed. Specimens impacted with different energy levels were analysed by a holographic procedure and by an electronic speckle pattern interferometry technique; the results obtained with the two techniques were compared with those acquired by a pulse-echo full-volume ultrasonic technique.

An encryption/decryption scheme based on a new Phase-Contrast Technique, without the use of a phase-changing plate (phase dielectric dot) on the Fourier plane of a 4f optical correlator is proposed. The encryption of a gray level image is achieved by multiplying the phase distribution obtained directly from the gray level image by a random phase distribution. The encoding is obtained without any iterative calculation to generate the encrypted phase-only mask. The robustness of the encoding is assured by the non-linearities intrinsic to the phase-contrast method and the random phase distribution used in the encryption process. The advantage of this method is the easy scheme to recover the gray level information from the decrypted phase-only mask applying the proposed Zero-Order Phase-contrast Technique.

This paper presents the utility of multipolarization Synthetic Aperture Radar (SAR) data for surface feature delineation and forest vegetation characterization. Three channels of ratioed data (VV/ HH, VH/HH, and VH/VV) are generated from the HH, VV, and VH polarization data (V = vertical, H = horizontal). The ratioed data are linearly stretched to yield a digital number within a range of 0 to 255. The techniques for reducing SAR speckle noise and for measuring the degree of separation are discussed. For surface feature delineation, the results indicate that cross polarization as well as cross polarization ratioed data better delineate those surface features that are difficult to separate by like polarization data. The results suggest using a median value filtering technique to reduce within-plot data fluctuation to increase the separability measure. For forest vegetation characterization, the results indicate that multipolarization SAR data may be used to estimate forest properties such as total-tree biomass, basal area, and tree height.

The paper presents a versatile wavelet domain despeckling technique to visually enhance the medical ultrasound (US) images for improving the clinical diagnosis. The method uses the two-sided generalized Nakagami distribution (GND) for modeling the speckle wavelet coefficients and the signal wavelet coefficients are approximated using the generalized Gaussian distribution (GGD). Combining these statistical priors with the Bayesian maximum a posteriori (MAP) criterion, the thresholding/shrinkage estimators are derived for processing the wavelet coefficients of detail subbands. Consequently, two blind speckle suppressors named as GNDThresh and GNDShrink have been implemented and evaluated on both the artificial speckle simulated images and real US images. The experimental results demonstrate the superiority of the suggested technique both quantitatively and qualitatively as compared to other competitive schemes reported in the image denoising literature, e.g., the proposed method yields a gain of more than 0.36 dB over the best state-of-the-art despeckling method (GenLik), 0.93 dB over SRAD filter, 2.35 dB over Lee filter, and 1.34 dB over Kuan filter in terms of signal-to-noise ratio, when tested on the realistic US images. The visual comparison of despeckled US images and the higher values of quality metrics (coefficient of correlation, edge preservation index, quality index, and structural similarity index) indicate that the new method suppresses the speckle noise well while preserving the texture and organ surfaces. Further, the proposed method will be evaluated on other class of images as well as by employing multiple observer evaluation.

This paper considers the fundamental mechanism by which speckle noise is generated in Laser Vibrometry before describing a new numerical simulation for prediction of speckle noise level in a real measurement. Factors within the simulation include rate of change of phase within individual speckle transitions, low pass filtering to match the frequency range of experimental data with which comparison is to be made, a track-and-hold facility for periods of low signal amplitude and wavefront curvature effects. The simulation data provides real insight into the phase and amplitude modulation of the Doppler signal and good agreement is found in the final comparison with experimental data from a measurement on a rotating target.

We present and characterize a sequential angular compounding method for reducing speckle contrast in optical coherence tomography images of paint layers. The results are compared with postprocessing methods, and we show that the compounding technique can improve the speckle contrast ratio in B-scans by better than a factor of 2 in exchange for a negligible loss of resolution. As a result, image aesthetics are improved, thin layers become more distinct, and edge-detection algorithms work more efficiently. The effect of varying the angular scan size and number of averages is investigated, and it is found that a degree of statistical correlation between speckle patterns exists, even for relatively large changes in angle of incidence. Angular compounding is also performed on three-dimensional data sets and compared with a method whereby en face slices are averaged over depth.

Objective Intravascular ultrasound (IVUS) is a diagnostic imaging technique for tomographic visualization of coronary arteries. Automatic analysis of IVUS images is difficult due to speckle noise, artifacts of the catheter, and shadows generated by calcifications. We designed and implemented a system for automated segmentation of coronary artery IVUS images. Methods Two methods for automatic detection of the intima and the media-adventitia borders in IVUS coronary artery images were developed and compared. The first method uses the parametric deformable models, while the second method is based on the geometric deformable models. The initial locations of the borders are approximated using two different edge detection methods. The final borders are then defined using the two deformable models. Finally, the calcified regions between the extracted borders are identified using a Bayesian classifier. The performance of the proposed methods was evaluated using 60 different IVUS images obtained from 7 patients. Results Segmented images were compared with manually outlined contours. We compared the performance of calcified region characterization methods using ROC analysis and by computing the sensitivity and specificity of the Bayesian classifier, thresholding, adaptive thresholding, and textural features. The Bayesian method performed best. Conclusion The results shows that the geometric deformable model outperforms the parametric deformable model for automated segmentation of IVUS coronary artery images.

Despite the increasing adoption of other imaging modalities, ultrasound guidance is widely used for surgical procedures and clinical imaging due to its low cost, non-invasiveness, and real-time visual feedback. Many ultrasound-guided procedures require ...

In this paper we present an anisotropic filter for speckle reduction in ultrasound images and an adaptation of the geodesic active contours technique for the segmentation of breast tumors. The anisotropic diffusion we propose is based on a texture description provided by a set of Gabor filters and allows reducing speckle noise while preserving edges. Furthermore, it is used to extract an initial pre-segmentation of breast tumors which is used as initialization for the active contours technique. This technique has been adapted to the characteristics of ultrasonography by adding certain texture-related terms which provide a better discrimination of the regions inside and outside the nodules. These terms allow obtaining a more accurate contour when the gradients are not high and uniform.

The basic problem in optical and digital holography is the presence of speckle noise in the reconstruction process, which reduces the signal-to-noise ratio (SNR). The presence of speckle noise is serious drawback in optical and digital holography since it substantially reduces the SNR in the reconstructed image. In this paper, we present wavelet filtering to improve SNR in the reconstructed images from digital holograms. Experimental results are presented. r

In this paper, a filtering technique based upon two-dimensional continuous wavelet transform (2D-CWT) is used to eliminate the low frequency components of fringe patterns. The filtered fringe patterns are subsequently demodulated using a standard Fourier transform profilometry (FTP) algorithm. This image pre-filtering stage improves the noise performance of the FTP algorithm and enables the FTP method to demodulate fringe patterns with larger bandwidths. Also, the 2D-CWT technique reduces speckle noise significantly. Moreover, only a single fringe pattern is required in this technique. The 2D-CWT algorithm is capable of separating low frequency terms from the high frequency terms that contain phase-modulated fringe information, even when both interfere, greatly, in the frequency domain. The proposed algorithm is tested, both via computer simulation and using real fringe patterns. This revealed the robustness of this algorithm and also demonstrably enables the demodulation of a wider range of fringe patterns using the FTP technique.

This study demonstrates a simple method for attenuating the speckle noise generated by coherent multiple-scattered photons in optical-coherence tomography images. The method could be included among the space-diversity techniques used for speckle reduction. It relies on displacing the sample along a weakly focused beam in the sample arm of the interferometer, acquiring a coherent image for each sample position and adding the individual images to form a compounded image. It is proven that the compounded image displays a reduction in the speckle noise generated by multiple scattered photons and an enhancement in the intensity signal caused by singlebackscattered photons. To evaluate its potential biomedical applications, the method is used to investigate in vitro a caries lesion affecting the enamel layer of a wisdom tooth. Because of the uncorrelated nature of the speckle noise the compounded image provides a better mapping of the lesion compared to a single (coherent) image. Crown