adaptive bayesian wavelet shrinkage abstract when fitting wavelet based models shrinkage of the empirical wavelet coefficients is an effective tool for denoising the data this article outlines a bayesian approach to shrinkage obtained by placing priors on the wavelet coefficients the prior for each coefficient consists of a mixture of two normal distributions with different standard deviations the simple and intuitive form of prior allows us to propose automatic choices of prior parameters these parameters are chosen adaptively according to the resolution level of the coefficients typically shrinking high resolution frequency coefficients more heavily assuming a good estimate of the background noise level we obtain closed form expressions for the posterior means and variances of the unknown wavelet coefficients the latter may be used to assess uncertainty in the reconstruction several examples are used to illustrate the method and comparisons are made with other shrinkage methods

multiscale methods for data on graphs and irregular multidimensional situations for regularly spaced one dimensional data wavelet shrinkage has proven to be a compelling method for non parametric function estimation we create three new multiscale methods that provide wavelet like transforms both for data arising on graphs and for irregularly spaced spatial data in more than one dimension the concept of scale still exists within these transforms but as a continuous quantity rather than dyadic levels further we adapt recent empirical bayesian shrinkage techniques to enable us to perform multiscale shrinkage for function estimation both on graphs and for irregular spatial data we demonstrate that our methods perform very well when compared with several other methods for spatial regression for both real and simulated data although we concentrate on multiscale shrinkage regression we present our new wavelet transforms as generic tools intended to be the basis of methods that might benefit from a multiscale representation of data either on graphs or for irregular spatial data

multiscale modeling and estimation of poisson processes with application to photon limited imaging many important problems in engineering and science are well modeled by poisson processes in many applications it is of great interest to accurately estimate the intensities underlying observed poisson data in particular this work is motivated by photon limited imaging problems this paper studies a new bayesian approach to poisson intensity estimation based on the haar wavelet transform it is shown that the haar transform provides a very natural and powerful framework for this problem using this framework a novel multiscale bayesian prior to model intensity functions is devised the new prior leads to a simple bayesian intensity estimation procedure furthermore we characterize the correlation behavior of the new prior and show that it has 1 f spectral characteristics the new framework is applied to photon limited image estimation and its potential to improve nuclear medicine imaging is examined

consensus of a group of mobile agents in three dimensions abstract this paper investigates the consensus problem of a three dimensional 3d version of the widely studied vicsek model where each member moves in 3d euclidean space with a constant speed with headings updated according to the average direction of neighbors in comparison with the original vicsek model each agent s heading is determined by two angle sequences interacting with each other one of which evolves according to a linear non homogeneous equation which makes the theoretical analysis quite complicated by analyzing the underlying structure of the system and relying on estimation of some characteristics concerning the initial states we will establish consensus results for 3d vicsek model under random framework without resorting to any a priori connectivity assumption

image denoising in mixed poisson gaussian noise we propose a general methodology pure let to design and optimize a wide class of transform domain thresholding algorithms for denoising images corrupted by mixed poisson gaussian noise we express the denoising process as a linear expansion of thresholds let that we optimize by relying on a purely data adaptive unbiased estimate of the mean squared error mse derived in a non bayesian framework pure poisson gaussian unbiased risk estimate we provide a practical approximation of this theoretical mse estimate for the tractable optimization of arbitrary transform domain thresholding we then propose a pointwise estimator for undecimated filterbank transforms which consists of subband adaptive thresholding functions with signal dependent thresholds that are globally optimized in the image domain we finally demonstrate the potential of the proposed approach through extensive comparisons with state of the art techniques that are specifically tailored to the estimation of poisson intensities we also present denoising results obtained on real images of low count fluorescence microscopy

lower bounds on the proportion of leaders needed for expected consensus of 3 d flocks this paper considers the consensus behavior of a spatially distributed 3 d dynamical network composed of heterogeneous agents leaders and followers in which the leaders have the preferred information about the destination while the followers do not have all followers move in a 3 d euclidean space with a given speed and with their headings updated according to the average velocity of the corresponding neighbors compared with the 2 d model a key point lies in how to analyze the dynamical behavior of a linear nonhomogeneous equation where the nonhomogeneous term strongly nonlinearly depends on the states of all agents using the network structure and the estimation of some characteristics for the initial states we present a proper decaying rate for the nonhomogeneous term and then establish lower bounds on the ratio of the number of leaders to the number of followers that is needed for the expected consensus by considering two cases 1 fixed speed and neighborhood radius and 2 variable speed and neighborhood radius with respect to the population size some simulation examples are given to justify the theoretical results

needles and straw in haystacks empirical bayes estimates of possibly sparse sequences an empirical bayes approach to the estimation of possibly sparse sequences observed in gaussian white noise is set out and investigated the prior considered is a mixture of an atom of probability at zero and a heavy tailed density y with the mixing weight chosen by marginal maximum likelihood in the hope of adapting between sparse and dense sequences if estimation is then carried out using the posterior median this is a random thresholding procedure other thresholding rules employing the same threshold can also be used probability bounds on the threshold chosen by the marginal maximum likelihood approach lead to overall risk bounds over classes of signal sequences of length n allowing for sparsity of various kinds and degrees the signal classes considered are nearly black sequences where only a proportion is allowed to be nonzero and sequences with normalized p norm bounded by for 0 and 0 1 simulations show excellent performance for appropriately chosen functions y the method is computationally tractable and software is available the extension to a modified thresholding method relevant to the estimation of very sparse sequences is also considered

wavelet domain filtering for photon imaging systems many imaging systems rely on photon detection as the basis of image formation one of the major sources of error in these systems is poisson noise due to the quantum nature of the photon detection process unlike additive gaussian white noise the variance of poisson noise is proportional to the underlying signal intensity and consequently separating signal from noise is a very difficult task in this paper we perform a novel gedankenexperiment to devise a new wavelet domain filtering procedure for noise removal in photon imaging systems the filter adapts to both the signal and the noise and balances the trade off between noise removal and excessive smoothing of image details designed using the statistical method of cross validation the filter is simultaneously optimal in a small sample predictive sum of squares sense and asymptotically optimal in the mean square error sense the filtering procedure has a simple interpretation as a joint edge detection estimation process moreover we derive an efficient algorithm for performing the filtering that has the same order of complexity as the fast wavelet transform itself the performance of the new filter is assessed with simulated data experiments and tested with actual nuclear medicine imagery

natural exponential families with quadratic variance functions statistical theory the normal poisson gamma binomial negative binomial and nefghs distributions are the six univariate natural exponential families nef with quadratic variance functions qvf this sequel to morris 1982 treats certain statistical topics that can be handled within this unified nef qvf formulation including unbiased estimation bhattacharyya and cramer rao lower bounds conditional distributions and moments quadratic regression conjugate prior distributions moments of conjugate priors and posterior distributions empirical bayes and g 2 minimax marginal distributions and their moments parametric empirical bayes and characterizations

minimum risk wavelet shrinkage operator for poisson image denoising the pixel values of images taken by an image sensor are said to be corrupted by poisson noise to date multiscale poisson image denoising techniques have processed haar frame and wavelet coefficients the modeling of coefficients is enabled by the skellam distribution analysis we extend these results by solving for shrinkage operators for skellam that minimizes the risk functional in the multiscale poisson image denoising setting the minimum risk shrinkage operator of this kind effectively produces denoised wavelet coefficients with minimum attainable l 2 error

active classification for pomdps a kalman like state estimator author s zois ds levorato m mitra u abstract 1991 2012 ieee the problem of state tracking with active observation control is considered for a system modeled by a discrete time finite state markov chain observed through conditionally gaussian measurement vectors the measurement model statistics are shaped by the underlying state and an exogenous control input which influence the observations quality exploiting an innovations approach an approximate minimum mean squared error mmse filter is derived to estimate the markov chain system state to optimize the control strategy the associated mean squared error is used as an optimization criterion in a partially observable markov decision process formulation a stochastic dynamic programming algorithm is proposed to solve for the optimal solution to enhance the quality of system state estimates approximate mmse smoothing estimators are also derived finally the performance of the proposed framework is illustrated on the problem of physical activity detection in wireless body sensing networks the power of the proposed framework lies within its ability to accommodate a broad spectrum of active classification applications including sensor management for object classification and tracking estimation of sparse signals and radar scheduling

synchronization of multi agent systems without connectivity assumptions multi agent systems arise from diverse fields in natural and artificial systems such as schooling of fish flocking of birds coordination of autonomous agents in multi agent systems a typical and basic situation is the case where each agent has the tendency to behave as other agents do in its neighborhood through computer simulations vicsek et al 1995 showed that such a simple local interaction rule can lead to a certain kind of cooperative phenomenon synchronization of the overall system if the initial states are randomly distributed and the size of the system population is large since this model is of fundamental importance in understanding the multi agent systems it has attracted much research attention in recent years in this paper we will present a comprehensive theoretical analysis for this class of multi agent systems under a random framework with large population but without imposing any connectivity assumptions as did in almost all of the previous investigations to be precise we will show that for any given and fixed model parameters concerning with the interaction radius r and the agents moving speed v the overall system will synchronize as long as the population size n is large enough furthermore to keep the synchronization property as the population size n increases both r and v can actually be allowed to decrease according to certain scaling rates

multiscale poisson data smoothing summary the paper introduces a framework for non linear multiscale decompositions of poisson data that have piecewise smooth intensity curves the key concept is conditioning on the sum of the observations that are involved in the computation of a given multiscale coefficient within this framework most classical wavelet thresholding schemes for data with additive homoscedastic noise can be used any family of wavelet transforms orthogonal biorthogonal or second generation can be incorporated in this framework our second contribution is to propose a bayesian shrinkage approach with an original prior for coefficients of this decomposition as such the method combines the advantages of the haar fisz transform with wavelet smoothing and bayesian multiscale likelihood models with additional benefits such as extendability towards arbitrary wavelet families simulations show an important reduction in average squared error of the output compared with the present techniques of anscombe or fisz variance stabilization or multiscale likelihood modelling

parametric empirical bayes inference theory and applications abstract this article reviews the state of multiparameter shrinkage estimators with emphasis on the empirical bayes viewpoint particularly in the case of parametric prior distributions some successful applications of major importance are considered recent results concerning estimates of error and confidence intervals are described and illustrated with data

asymptotically optimal nonlinear mmse multiuser detection based on multivariate gaussian approximation in this paper a class of nonlinear minimum mean squared error multiuser detectors is derived based on a multivariate gaussian approximation of the multiple access interference for large systems this approach leads to expressions identical to those describing the probabilistic data association pda detector thus providing an alternative analytical justification for this structure a simplification to the pda detector based on approximating the covariance matrix of the multivariate gaussian distribution is suggested resulting in a soft interference cancellation scheme corresponding multiuser soft input soft output detectors delivering extrinsic log likelihood ratios are derived for application in iterative multiuser decoders finally a large system performance analysis is conducted for the simplified pda showing that the bit error rate ber performance of this detector can be accurately predicted and related to the replica method analysis for the optimal detector methods from statistical neurodynamics are shown to provide a closely related alternative large system prediction numerical results demonstrate that for large systems the ber is accurately predicted by the analysis and found to be close to optimal performance

multiscale variance stabilization via maximum likelihood this article proposes maximum likelihood approaches for multiscale variance stabilization mvs tranforms for independently and identically distributed data for two mvs transforms we present new unified theoretical results on their jacobians which are a key component of the likelihood the new results provide i a deeper theoretical understanding of the transforms and ii the ability to compute the likelihood in linear time compared to previous numerical methods which required quadratic effort the new mvs transforms are shown empirically to compare favourably to the well known box cox transform and almost dominate it we show how the new transforms like box cox can facilitate simpler models by an example involving the famous wool data

a new family of soft output adaptive receivers exploiting nonlinear mmse estimates for tdma based wireless links decision directed adaptive receivers suffer performance degradation on time varying and intersymbol interference impaired links because of two major problems the use of predicted channel estimates due to the unavoidable decision delay of any detector and the unreliability of hard decisions used for channel estimation and tracking it is shown here that combining a recursive nonlinear symbol estimator with a channel estimator with a low prediction order may alleviate this performance degradation in particular it is here proposed to employ the nonlinear minimum mean square error nl mmse filtered and fixed lag smoothed estimates of the transmitted symbols in place of the usual hard decisions for channel estimation and tracking it is also shown that these nl mmse estimates can be recursively computed on the basis of a linear transformation of the vector of the a posteriori probabilities apps of the states of the channel this approach allows the prediction order of the channel estimates to be limited and at the same time limits the performance degradation due to erroneous hard decisions another result presented here is that the use of nl mmse estimates in place of hard decisions is not based on mere intuition only but is a straightforward consequence of the statement of the problem of mmse channel estimation when the overly optimistic assumption of correct decisions is dropped on the basis of this novel approach a new family of soft output adaptive receivers is presented for time division multiple access based radio communications the proposed family of adaptive receivers is based on an app computer and exploits the apps for both channel estimation and detection the versatility of the apps ensures that the architecture of the proposed receiver is flexible so that several estimators and detectors can be embedded in it

wavelet shrinkage for natural exponential families with quadratic variance functions we propose a wavelet shrinkage methodology for univariate natural exponential families with quadratic variance functions covering the gaussian poisson gamma binomial negative binomial and generalised hyperbolic secant distributions simulation studies for poisson and binomial data are used to illustrate the usefulness of the proposed methodology and comparisons are made with other methods available in the literature we also present applications to datasets arising from high energy astrophysics and from epidemiology

this is spiral tap sparse poisson intensity reconstruction algorithms theory and practice observations in many applications consist of counts of discrete events such as photons hitting a detector which cannot be effectively modeled using an additive bounded or gaussian noise model and instead require a poisson noise model as a result accurate reconstruction of a spatially or temporally distributed phenomenon f from poisson data y cannot be effectively accomplished by minimizing a conventional penalized least squares objective function the problem addressed in this paper is the estimation of f from y in an inverse problem setting where the number of unknowns may potentially be larger than the number of observations and f admits sparse approximation the optimization formulation considered in this paper uses a penalized negative poisson log likelihood objective function with nonnegativity constraints since poisson intensities are naturally nonnegative in particular the proposed approach incorporates key ideas of using separable quadratic approximations to the objective function at each iteration and penalization terms related to l1 norms of coefficient vectors total variation seminorms and partition based multiscale estimation methods

skellam shrinkage wavelet based intensity estimation for inhomogeneous poisson data the ubiquity of integrating detectors in imaging and other applications implies that a variety of real world data are well modeled as poisson random variables whose means are in turn proportional to an underlying vector valued signal of interest in this article we first show how the so called skellam distribution arises from the fact that haar wavelet and filterbank transform coefficients corresponding to measurements of this type are distributed as sums and differences of poisson counts we then provide two main theorems on skellam shrinkage one showing the near optimality of shrinkage in the bayesian setting and the other providing for unbiased risk estimation in a frequentist context these results serve to yield new estimators in the haar transform domain including an unbiased risk estimate for shrinkage of haar fisz variance stabilized data along with accompanying low complexity algorithms for inference we conclude with a simulation study demonstrating the efficacy of our skellam shrinkage estimators both for the standard univariate wavelet test functions as well as a variety of test images taken from the image processing literature confirming that they offer some performance improvements over existing alternatives

nonparametric regression in exponential families most results in nonparametric regression theory are developed only for the case of additive noise in such a setting many smoothing techniques including wavelet thresholding methods have been developed and shown to be highly adaptive in this paper we consider nonparametric regression in exponential families with the main focus on the natural exponential families with a quadratic variance function which include for example poisson regression binomial regression and gamma regression we propose a unified approach of using a mean matching variance stabilizing transformation to turn the relatively complicated problem of nonparametric regression in exponential families into a standard homoscedastic gaussian regression problem then in principle any good nonparametric gaussian regression procedure can be applied to the transformed data to illustrate our general methodology in this paper we use wavelet block thresholding to construct the final estimators of the regression function the procedures are easily implementable both theoretical and numerical properties of the estimators are investigated the estimators are shown to enjoy a high degree of adaptivity and spatial adaptivity with near optimal asymptotic performance over a wide range of besov spaces the estimators also perform well numerically

adapting to unknown smoothness via wavelet shrinkage abstract we attempt to recover a function of unknown smoothness from noisy sampled data we introduce a procedure sureshrink that suppresses noise by thresholding the empirical wavelet coefficients the thresholding is adaptive a threshold level is assigned to each dyadic resolution level by the principle of minimizing the stein unbiased estimate of risk sure for threshold estimates the computational effort of the overall procedure is order n log n as a function of the sample size n sureshrink is smoothness adaptive if the unknown function contains jumps then the reconstruction essentially does also if the unknown function has a smooth piece then the reconstruction is essentially as smooth as the mother wavelet will allow the procedure is in a sense optimally smoothness adaptive it is near minimax simultaneously over a whole interval of the besov scale the size of this interval depends on the choice of mother wavelet we know from a previous paper by the authors that traditional smoot

bayesian multiscale models for poisson processes abstract i introduce a class of bayesian multiscale models bmsm s for one dimensional inhomogeneous poisson processes the focus is on estimating the discretized intensity function underlying the process unlike the usual transform based approach at the heart of most wavelet based methods for gaussian data these bmsm s are constructed using recursive dyadic partitions rdp s within an entirely likelihood based framework each rdp may be associated with a binary tree and a new multiscale prior distribution is introduced for the unknown intensity through the placement of mixture distributions at each of the nodes of the tree the concept of model mixing is then applied to a complete collection of such trees in addition to allowing for the inclusion of full location scale information in the model this last step also is fundamental both in inducing stationarity in the prior distribution and in enabling a given intensity function to be approximated at the resolution of the data under squared error lo

iterative shrinkage approach to restoration of optical imagery the problem of reconstruction of digital images from their degraded measurements is regarded as a problem of central importance in various fields of engineering and imaging sciences in such cases the degradation is typically caused by the resolution limitations of an imaging device in use and or by the destructive influence of measurement noise specifically when the noise obeys a poisson probability law standard approaches to the problem of image reconstruction are based upon using fixed point algorithms which follow the methodology first proposed by richardson and lucy the practice of using these methods however shows that their convergence properties tend to deteriorate at relatively high noise levels accordingly in the present paper a novel method for denoising and or deblurring of digital images corrupted by poisson noise is introduced the proposed method is derived under the assumption that the image of interest can be sparsely represented in the domain of a linear transform consequently a shrinkage based iterative procedure is proposed which guarantees the solution to converge to the global maximizer of an associated maximum a posteriori criterion it is shown in a series of computer simulated experiments that the proposed method outperforms a number of existing alternatives in terms of stability precision and computational efficiency

orthonormal bases of compactly supported wavelets we construct orthonormal bases of compactly supported wavelets with arbitrarily high regularity the order of regularity increases linearly with the support width we start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction the construction then follows from a synthesis of these different approaches

wavelets ridgelets and curvelets for poisson noise removal in order to denoise poisson count data we introduce a variance stabilizing transform vst applied on a filtered discrete poisson process yielding a near gaussian process with asymptotic constant variance this new transform which can be deemed as an extension of the anscombe transform to filtered data is simple fast and efficient in very low count situations we combine this vst with the filter banks of wavelets ridgelets and curvelets leading to multiscale vsts ms vsts and nonlinear decomposition schemes by doing so the noise contaminated coefficients of these ms vst modified transforms are asymptotically normally distributed with known variances a classical hypothesis testing framework is adopted to detect the significant coefficients and a sparsity driven iterative scheme reconstructs properly the final estimate a range of examples show the power of this ms vst approach for recovering important structures of various morphologies in very low count images these results also demonstrate that the ms vst approach is competitive relative to many existing denoising methods

patch based nonlocal functional for denoising fluorescence microscopy image sequences we present a nonparametric regression method for denoising 3 d image sequences acquired via fluorescence microscopy the proposed method exploits the redundancy of the 3 d time information to improve the signal to noise ratio of images corrupted by poisson gaussian noise a variance stabilization transform is first applied to the image data to remove the dependence between the mean and variance of intensity values this preprocessing requires the knowledge of parameters related to the acquisition system also estimated in our approach in a second step we propose an original statistical patch based framework for noise reduction and preservation of space time discontinuities in our study discontinuities are related to small moving spots with high velocity observed in fluorescence video microscopy the idea is to minimize an objective nonlocal energy functional involving spatio temporal image patches the minimizer has a simple form and is defined as the weighted average of input data taken in spatially varying neighborhoods the size of each neighborhood is optimized to improve the performance of the pointwise estimator the performance of the algorithm which requires no motion estimation is then evaluated on both synthetic and real image sequences using qualitative and quantitative criteria

bayesian tree structured image modeling using wavelet domain hidden markov models wavelet domain hidden markov models have proven to be useful tools for statistical signal and image processing the hidden markov tree hmt model captures the key features of the joint probability density of the wavelet coefficients of real world data one potential drawback to the hmt framework is the need for computationally expensive iterative training to fit an hmt model to a given data set e g using the expectation maximization algorithm we greatly simplify the hmt model by exploiting the inherent self similarity of real world images the simplified model specifies the hmt parameters with just nine meta parameters independent of the size of the image and the number of wavelet scales we also introduce a bayesian universal hmt uhmt that fixes these nine parameters the uhmt requires no training of any kind while extremely simple we show using a series of image estimation denoising experiments that these new models retain nearly all of the key image structure modeled by the full hmt finally we propose a fast shift invariant hmt estimation algorithm that outperforms other wavelet based estimators in the current literature both visually and in mean square error

multiscale likelihood analysis and complexity penalized estimation we describe here a framework for a certain class of multiscale likelihood factorizations wherein in analogy to a wavelet decomposition of an l 2 function a given likelihood function has an alternative representation as a product of conditional densities reflecting information in both the data and the parameter vector localized in position and scale the framework is developed as a set of sufficient conditions for the existence of such factorizations formulated in analogy to those underlying a standard multiresolution analysis for wavelets and hence can be viewed as a multiresolution analysis for likelihoods we then consider the use of these factorizations in the task of nonparametric complexity penalized likelihood estimation we study the risk properties of certain thresholding and partitioning estimators and demonstrate their adaptivity and near optimality in a minimax sense over a broad range of function spaces based on squared hellinger distance as a loss function in particular our results provide an illustration of how properties of classical wavelet based estimators can be obtained in a single unified framework that includes models for continuous count and categorical data types

a proximal iteration for deconvolving poisson noisy images using sparse representations we propose an image deconvolution algorithm when the data is contaminated by poisson noise the image to restore is assumed to be sparsely represented in a dictionary of waveforms such as the wavelet or curvelet transforms our key contributions are as follows first we handle the poisson noise properly by using the anscombe variance stabilizing transform leading to a nonlinear degradation equation with additive gaussian noise second the deconvolution problem is formulated as the minimization of a convex functional with a data fidelity term reflecting the noise properties and a nonsmooth sparsity promoting penalty over the image representation coefficients e g lscr1 norm an additional term is also included in the functional to ensure positivity of the restored image third a fast iterative forward backward splitting algorithm is proposed to solve the minimization problem we derive existence and uniqueness conditions of the solution and establish convergence of the iterative algorithm finally a gcv based model selection procedure is proposed to objectively select the regularization parameter experimental results are carried out to show the striking benefits gained from taking into account the poisson statistics of the noise these results also suggest that using sparse domain regularization may be tractable in many deconvolution applications with poisson noise such as astronomy and microscopy

wavelet thresholding via a bayesian approach we discuss a bayesian formalism which gives rise to a type of wavelet threshold estimation in nonparametric regression a prior distribution is imposed on the wavelet coefficients of the unknown response function designed to capture the sparseness of wavelet expansion that is common to most applications for the prior specified the posterior median yields a thresholding procedure our prior model for the underlying function can be adjusted to give functions falling in any specific besov space we establish a relationship between the hyperparameters of the prior model and the parameters of those besov spaces within which realizations from the prior will fall such a relationship gives insight into the meaning of the besov space parameters moreover the relationship established makes it possible in principle to incorporate prior knowledge about the function s regularity properties into the prior model for its wavelet coefficients however prior knowledge about a function s regularity properties might be difficult to elicit with this in mind we propose a standard choice of prior hyperparameters that works well in our examples several simulated examples are used to illustrate our method and comparisons are made with other thresholding methods we also present an application to a data set that was collected in an anaesthesiological study

support value based stent graft marker detection with the development of the fluoroscopic roentgenographic stereophotogrammetric analysis frsa it is possible to make the three dimensional 3d dynamics of stent graft the stent graft markers however are identified manually in this paper we present a robust solution for automatic detection of stent graft marker projections in frsa x ray images several directional support value dsv filters and the directional support value transform dsvt method are studied based on the dsv of the dsvt a support value matrix is constructed and the determinant of this matrix is then defined as the markerness measure the corresponding multi scale correlations of the rescaled markerness measures are computed for enhancing the multi scale marker response peaks while suppressing the effects of stent grafts and poisson noise the marker spots are subsequently located by finding the local maximum of the correlated markerness measures the conditional variance stabilizer cvs is further integrated into this framework for removing poisson noises performance comparisons are carried out among the proposed dsvt the cvs dsvt local threshold operation lto and the frequently adopted spot detectors including the morphological grayscale opening top hat filter mth wavelet multiscale products wmp and multiscale variance stabilizing transform msvst methods the results from experiments on synthetic as well as real frsa x ray image data show that the proposed cvs dsvt method performs better than other detectors in terms of the free response receiver operation characteristic froc curves