shape preserving local histogram modification a novel approach for shape preserving contrast enhancement is presented in this paper contrast enhancement is achieved by means of a local histogram equalization algorithm which preserves the level sets of the image this basic property is violated by common local schemes thereby introducing spurious objects and modifying the image information the scheme is based on equalizing the histogram in all the connected components of the image which are defined based both on the grey values and spatial relations between pixels in the image and following mathematical morphology constitute the basic objects in the scene we give examples for both grey value and color images

exact histogram specification while in the continuous case statistical models of histogram equalization specification would yield exact results their discrete counterparts fail this is due to the fact that the cumulative distribution functions one deals with are not exactly invertible otherwise stated exact histogram specification for discrete images is an ill posed problem invertible cumulative distribution functions are obtained by translating the problem in a k dimensional space and further inducing a strict ordering among image pixels the proposed ordering refines the natural one experimental results and statistical models of the induced ordering are presented and several applications are discussed image enhancement normalization watermarking etc

an energy based model for the image edge histogram specification problem in this correspondence we present an original energy based model that achieves the edge histogram specification of a real input image and thus extends the exact specification method of the image luminance or gray level distribution recently proposed by coltuc et al our edge histogram specification approach is stated as an optimization problem in which each edge of a real input image will tend iteratively toward some specified gradient magnitude values given by a target edge distribution or a normalized edge histogram possibly estimated from a target image to this end a hybrid optimization scheme combining a global and deterministic conjugate gradient based procedure and a local stochastic search using the metropolis criterion is proposed herein to find a reliable solution to our energy based model experimental results are presented and several applications follow from this procedure

image enhancement by entropy maximization and quantization resolution upconversion this article introduces a new contrast enhancement algorithm of tone preserving entropy maximization its design objective is to present the maximal amount of information content in the enhanced image or being optimal in an information theoretical sense while preventing the loss of tone continuity the resulting optimization problem can be graph theoretically modeled as the construction of the k edges maximum weight path and it can be solved efficiently by dynamic programming moreover the proposed algorithm is made more effective by being combined with a preprocess of image restoration that aims to correct quantization errors caused by the analog to digital conversion of image signals empirical evidences are provided to demonstrate the superior visual quality obtained by the new image enhancement algorithm

instrumental variable quantile regression a robust inference approach abstract in this paper we develop robust inference procedures for an instrumental variables model defined by y d u where d u is strictly increasing in u and u is a uniform variable that may depend on d but is independent of a set of instrumental variables z the proposed inferential procedures are computationally convenient in typical applications and can be carried out using software available for ordinary quantile regression our inferential procedure arises naturally from an estimation algorithm and has the important feature of being robust to weak and partial identification and remains valid even in cases where identification fails completely the use of the proposed procedures is illustrated through two empirical examples

smoothed gmm for quantile models abstract we consider estimation of finite dimensional parameters identified by general conditional quantile restrictions including instrumental variables quantile regression within a generalized method of moments framework moment functions are smoothed to aid both computation and precision consistency and asymptotic normality are established under weaker assumptions than previously seen in the literature allowing dependent data and nonlinear structural models simulations illustrate the finite sample properties an in depth empirical application estimates the consumption euler equation derived from quantile utility maximization advantages of quantile euler equations include robustness to fat tails decoupling risk attitude from the elasticity of intertemporal substitution and error free log linearization

asymptotic normality of p norm estimators in multiple regression the consistency and asymptotic normality of p norm estimators 1 p 2 is established by applying some of the ideas of huber 1973 where asymptotic normality of the so called m estimators for regression parameters is shown a central role is played by a weight function huber assumed that and are bounded this is however not the case for p norm estimators with 1 p 2 but some of his ideas can still be applied

testing a parametric quantile regression model with an endogenous explanatory variable against a nonparametric alternative abstract this paper is concerned with inference about a function g that is identified by a conditional quantile restriction involving instrumental variables the paper presents a test of the hypothesis that g belongs to a finite dimensional parametric family against a nonparametric alternative the test is not subject to the ill posed inverse problem of nonparametric instrumental variable estimation under mild conditions the test is consistent against any alternative model in large samples its power is arbitrarily close to 1 uniformly over a class of alternatives whose distance from the null hypothesis is proportional to n 1 2 where n is the sample size monte carlo simulations illustrate the finite sample performance of the test

texture enhanced histogram equalization using tv rm l 1 image decomposition histogram transformation defines a class of image processing operations that are widely applied in the implementation of data normalization algorithms in this paper we present a new variational approach for image enhancement that is constructed to alleviate the intensity saturation effects that are introduced by standard contrast enhancement ce methods based on histogram equalization in this paper we initially apply total variation tv minimization with a l1 fidelity term to decompose the input image with respect to cartoon and texture components contrary to previous papers that rely solely on the information encompassed in the distribution of the intensity information in this paper the texture information is also employed to emphasize the contribution of the local textural features in the ce process this is achieved by implementing a nonlinear histogram warping ce strategy that is able to maximize the information content in the transformed image our experimental study addresses the ce of a wide variety of image data and comparative evaluations are provided to illustrate that our method produces better results than conventional ce strategies

adaptive image contrast enhancement using generalizations of histogram equalization this paper proposes a scheme for adaptive image contrast enhancement based on a generalization of histogram equalization he he is a useful technique for improving image contrast but its effect is too severe for many purposes however dramatically different results can be obtained with relatively minor modifications a concise description of adaptive he is set out and this framework is used in a discussion of past suggestions for variations on he a key feature of this formalism is a cumulation function which is used to generate a grey level mapping from the local histogram by choosing alternative forms of cumulation function one can achieve a wide variety of effects a specific form is proposed through the variation of one or two parameters the resulting process can produce a range of degrees of contrast enhancement at one extreme leaving the image unchanged at another yielding full adaptive equalization

fast ordering algorithm for exact histogram specification this paper provides a fast algorithm to order in a meaningful strict way the integer gray values in digital quantized images it can be used in any exact histogram specification based application our algorithm relies on the ordering procedure based on the specialized variational approach this variational method was shown to be superior to all other state of the art ordering algorithms in terms of faithful total strict ordering but not in speed indeed the relevant functionals are in general difficult to minimize because their gradient is nearly flat over vast regions in this paper we propose a simple and fast fixed point algorithm to minimize these functionals the fast convergence of our algorithm results from known analytical properties of the model our algorithm is equivalent to an iterative nonlinear filtering furthermore we show that a particular form of the variational model gives rise to much faster convergence than other alternative forms we demonstrate that only a few iterations of this filter yield almost the same pixel ordering as the minimizer thus we apply only few iteration steps to obtain images whose pixels can be ordered in a strict and faithful way numerical experiments confirm that our algorithm outperforms by far its main competitors

censored regression quantiles abstract the object of this paper is to demonstrate how the lad estimation method for the censored regression model can be extended to more general quantiles in this paper the form of the conditional quantiles for the censored regression models is heuristically derived and discussed the resulting estimators of the regression coefficients which include the censored lad estimator as a special case are shown to be consistent and asymptotically normally distributed under appropriately translated versions of the corresponding assumptions for the former approach consistent estimation of the asymptotic covariance matrix when the error terms are i i d is also treated the paper discussed how several quantile estimators might be combined to improve efficiency when the error terms are identically distributed and how tests of homoskedasticity and symmetry of the error distribution can be constructed using particular differences of the estimated coefficients

nonparametric instrumental variables estimation of a quantile regression model we consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression error conditional on an instrumental variable to be zero the resulting estimating equation is a nonlinear integral equation of the first kind which generates an ill posed inverse problem the integral operator and distribution of the instrumental variable are unknown and must be estimated nonparametrically we show that the estimator is mean square consistent derive its rate of convergence in probability and give conditions under which this rate is optimal in a minimax sense the results of monte carlo experiments show that the estimator behaves well in finite samples

smoothed quantile regression for panel data abstract this paper studies fixed effects estimation of quantile regression models for panel data under an asymptotic framework where both the numbers of individuals and time periods grow at the same rate we show that the fixed effects estimator for the smoothed objective function has a limiting normal distribution with a bias in the mean and provide the analytic form of the asymptotic bias we propose a one step bias correction estimator based on the analytic bias formula obtained from the asymptotic analysis importantly our results cover the case that observations are dependent over time we illustrate the effects of the bias correction through simulations

resistant estimation for simultaneous equations models using weighted instrumental variables in this paper we present a weighted instrumental variables estimator that is resistant2 to heavy tailed errors aberrant data in either the endogenous or exogenous variables and certain other model failures the estimator is analogous to the weighted least squares approach to robustness proposed by krasker and welsch 21 for ordinary regression we will discuss the theory that motivated this estimator derive some of its properties describe our computational algorithm and using an empirical example illustrate the estimator s utility as a tool for data analysis in structural models the evolution of robust estimators for simultaneous equations models has closely resembled the development of robust methods for ordinary regression in both cases research focused at first on the well documented effects of long tailed error distributions on the classical procedures for ordinary regression models statisticians have studied the properties of least absolute deviations lad estimators see bassett and koenker 2 and amemiya 1 and maximum likelihood type estimators called m estimators see huber 14 for simultaneous equations models lad can be generalized in several different ways to modify two stage least squares or instrumental variables amemiya 1 has presented these estimators in a unified framework and powell 31 has proven their asymptotic normality under weak assumptions fair 8 has compared two stage lad estimates of a u s macroeconomic model with two stage least squares and full information maximum likelihood estimates the m estimator concept can also be generalized to simultaneous equations for example prucha and kelejian 32 have considered maximum likelihood estimation under the assumption that the disturbance vector is multivariate student t although lad and m estimators maintain a high efficiency when the error distribution is heavy tailed they are not robust in the stronger sense of hampel 10 which roughly speaking requires an estimator to have a limited sensitivity to any small fraction of the data 3 lad and m estimators fail under this criterion because a single observation whose values for the right side variables are highly anomalous can have an arbitrarily large effect on lad estimates or m estimates just as on the classical procedures consequently these estimators do not provide insurance against the sort of gross errors that occur in some data sets nor do they serve as reliable diagnostics for departures from linearity occurring in extreme regions of the space of right side variables for linear models the estimators that satisfy the strong hampel robustness criterion have become known as bounded influence estimators several such estimators have been

conditional empirical likelihood estimation and inference for quantile regression models abstract this paper considers two empirical likelihood based estimation inference and specification testing methods for quantile regression models first we apply the method of conditional empirical likelihood cel by kitamura et al 2004 empirical likelihood based inference in conditional moment restriction models econometrica 72 1667 1714 and zhang and gijbels 2003 sieve empirical likelihood and extensions of the generalized least squares scandinavian journal of statistics 30 1 24 to quantile regression models second to avoid practical problems of the cel method induced by the discontinuity in parameters of cel we propose a smoothed counterpart of cel called smoothed conditional empirical likelihood scel we derive asymptotic properties of the cel and scel estimators parameter hypothesis tests and model specification tests important features are i the cel and scel estimators are asymptotically efficient and do not require preliminary weight estimation ii by inverting the cel and scel ratio parameter hypothesis tests asymptotically valid confidence intervals can be obtained without estimating the asymptotic variances of the estimators and iii in contrast to cel the scel method can be implemented by some standard newton type optimization simulation results demonstrate that the scel method in particular compares favorably with existing alternatives

quantile regression methods for recursive structural equation models abstract two classes of quantile regression estimation methods for the recursive structural equation models of chesher 2003 identification in nonseparable models econometrica 71 1405 1441 are investigated a class of weighted average derivative estimators based directly on the identification strategy of chesher is contrasted with a new control variate estimation method the latter imposes stronger restrictions achieving an asymptotic efficiency bound with respect to the former class an application of the methods to the study of the effect of class size on the performance of dutch primary school students shows that i reductions in class size are beneficial for good students in language and for weaker students in mathematics ii larger classes appear beneficial for weaker language students and iii the impact of class size on both mean and median performance is negligible

least absolute deviations estimation for the censored regression model this paper proposes an alternative to maximum likelihood estimation of the parameters of the censored regression or censored tobit model the proposed estimator is a generalization of least absolute deviations estimation for the standard linear model and unlike estimation methods based on the assumption of normally distributed error terms the estimator is consistent and asymptotically normal for a wide class of error distributions and is also robust to heteroscedasticity the paper gives the regularity conditions and proofs of these large sample results and proposes classes of consistent estimators of the asymptotic covariance matrix for both homoscedastic and heteroscedastic disturbances

the neumann solution to the inhomogeneous cauchy riemann equation in the ball in ⁿ let denote the formal adjoint of the cauchy riemann operator a on c 0 and let n denote the kohn neummn operator on the unit ball in cn the operator o n provides a natural fundamental solution for af g on the ball it is our purpose to present the kernel p for this operator o x explicitly the coeffi cients are exhibited as rational functions 1 introduction the 8 neumann problem was proposed by spencer the motiva tion for the problem comes from a desire to solve the inhomogeneous cauchy riemann equations this problem was successfully carried through by kohn see k and fk however none of the kernels shown to exist by kohn are explictly known even in the simplest cases it is our purpose to present one of these kernels explicitly the coefficients of the kernel are rational functions let d c cn denote the unit ball let denote the formal adjoint of a with respect to the euclidean metric on d and let n denote the kohn neumann operator see 2 for the defining properties of n the operator p o n is a modified fundamental solution for a on d i e if k is the bergman projection and if f is a smooth form on d then

joint exact histogram specification and image enhancement through the wavelet transform histogram specification equalization is an important tool for tasks such as image enhancement and normalization although this problem has exact solution for continuous images in the case of digital images it is ill posed recently an exact pixel ordering method based solely on local image intensity was proposed yet still with some limitations especially since it ignores the important image edge information in this paper we present a wavelet based method that simultaneously achieves the exact histogram specification and good image enhancement performance it does so through a carefully designed strict pixel ordering process during which the wavelet coefficients are fine tuned for the image enhancement purpose compared to previous work this approach takes into account not only local mean intensity values but also local edge information other advantages include fast pixel ordering good statistical models and better image enhancement performance experimental results and comparison with state of the art methods are presented

wavelet based statistical signal processing using hidden markov models wavelet based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly gaussian these models are unrealistic for many real world signals we develop a new framework for statistical signal processing based on wavelet domain hidden markov models hmms that concisely models the statistical dependencies and non gaussian statistics encountered in real world signals wavelet domain hmms are designed with the intrinsic properties of the wavelet transform in mind and provide powerful yet tractable probabilistic signal models efficient expectation maximization algorithms are developed for fitting the hmms to observational signal data the new framework is suitable for a wide range of applications including signal estimation detection classification prediction and even synthesis to demonstrate the utility of wavelet domain hmms we develop novel algorithms for signal denoising classification and detection

two stage least absolute deviations estimators1 in this paper the method of least absolute deviations is applied to the estimation of the parameters of a structural equation in the simultaneous equations model a class of estimators called two stage least absolute deviations estimators is defined their asymptotic properties are derived and the problem of finding the optimal member of the class is considered in this paper we apply the method of least absolute deviations to the estimation of the parameters of a structural equation in the simultaneous equations model we define a class of estimators called two stage least absolute deviations estimators 2slad and derive their asymptotic properties they are so named as their relationship to the two stage least squares estimator 2sls is analogous to the relationship of the least absolute deviations estimator lad to the least squares estimator ls in the standard regression model the lad estimation has been extensively studied in the context of the standard regression model and its usefulness is universally recognized in this paper we show that the advantage of 2slad over 2sls in the simultaneous equations model can be as great as that of lad over ls in the standard regression model if 2slad is properly defined this last clause is very important since the results of this paper indicate that the lad analogue of 2sls that has been considered before in the literature is not an appropriate method

robust estimators for simultaneous equations models this paper presents a class of robust estimators for linear and non linear simultaneous equations models which are a direct generalization of the maximum likelihood estimator the new estimators are obtained as solutions of a generalized likelihood equation they are resistant to deviations from the model distribution to outlying observations and to some model misspecifications an optimality principle leads to the construction of an optimal robust estimator which is the best trade off between efficiency at the model and robustness

a theory for multiresolution signal decomposition the wavelet representation multiresolution representations are effective for analyzing the information content of images the properties of the operator which approximates a signal at a given resolution were studied it is shown that the difference of information between the approximation of a signal at the resolutions 2 sup j 1 and 2 sup j where j is an integer can be extracted by decomposing this signal on a wavelet orthonormal basis of l sup 2 r sup n the vector space of measurable square integrable n dimensional functions in l sup 2 r a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi x this decomposition defines an orthogonal multiresolution representation called a wavelet representation it is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters wavelet representation lies between the spatial and fourier domains for images the wavelet representation differentiates several spatial orientations the application of this representation to data compression in image coding texture discrimination and fractal analysis is discussed