nondifferentiable multiobjective symmetric duality with f convexity over cones abstract in the present paper a pair of wolfe type nondifferentiable multiobjective second order symmetric dual programs over arbitrary cones are formulated using the concept of weak efficiency with respect to a convex cone weak strong and converse duality theorems are studied under second order k f convexity assumptions self duality is also discussed

brief paper output regulation for linear distributed parameter systems using finite dimensional dual observers in this article the solution of the output regulation problem is considered for linear infinite dimensional systems where the outputs to be controlled cannot be measured it is shown that this problem can be solved by a finite dimensional dual observer that is directly implementable so that the separation principle can be applied for the stabilization as in finite dimensions a parametric design of these dual observers is proposed for riesz spectral systems that allows to achieve a low controller order and a desired control performance for the closed loop system the presented results are illustrated by determining a finite dimensional regulator for an euler bernoulli beam with kelvin voigt damping that achieves tracking for steplike reference inputs and that asymptotically rejects sinusoidal disturbances

multiobjective symmetric duality involving cones abstract in this paper a pair of multiobjective symmetric dual programs over arbitrary cones are formulated for cone convex functions weak strong converse and self duality theorems are proved for these programs

symmetric dual multiobjective programming abstract in this paper a pair of symmetric dual multiobjective programming problems is formulated and the duality theorems are established for pseudo convex pseudo concave functions

high dimensional camera shake removal with given depth map camera motion blur is drastically nonuniform for large depth range scenes and the nonuniformity caused by camera translation is depth dependent but not the case for camera rotations to restore the blurry images of large depth range scenes deteriorated by arbitrary camera motion we build an image blur model considering 6 degrees of freedom dof of camera motion with a given scene depth map to make this 6d depth aware model tractable we propose a novel parametrization strategy to reduce the number of variables and an effective method to estimate high dimensional camera motion as well the number of variables is reduced by temporal sampling motion function which describes the 6 dof camera motion by sampling the camera trajectory uniformly in time domain to effectively estimate the high dimensional camera motion parameters we construct the probabilistic motion density function pmdf to describe the probability distribution of camera poses during exposure and apply it as a unified constraint to guide the convergence of the iterative deblurring algorithm specifically pmdf is computed through a back projection from 2d local blur kernels to 6d camera motion parameter space and robust voting we conduct a series of experiments on both synthetic and real captured data and validate that our method achieves better performance than existing uniform methods and nonuniform methods on large depth range scenes

regularization of rif blind image deconvolution blind image restoration is the process of estimating both the true image and the blur from the degraded image using only partial information about degradation sources and the imaging system our main interest concerns optical image enhancement where the degradation often involves a convolution process we provide a method to incorporate truncated eigenvalue and total variation regularization into a nonlinear recursive inverse filter rif blind deconvolution scheme first proposed by kundar and by kundur and hatzinakos 1996 1998 tests are reported on simulated and optical imaging problems

a soft double regularization approach to parametric blind image deconvolution this paper proposes a blind image deconvolution scheme based on soft integration of parametric blur structures conventional blind image deconvolution methods encounter a difficult dilemma of either imposing stringent and inflexible preconditions on the problem formulation or experiencing poor restoration results due to lack of information this paper attempts to address this issue by assessing the relevance of parametric blur information and incorporating the knowledge into the parametric double regularization pdr scheme the pdr method assumes that the actual blur satisfies up to a certain degree of parametric structure as there are many well known parametric blurs in practical applications further it can be tailored flexibly to include other blur types if some prior parametric knowledge of the blur is available a manifold soft parametric modeling technique is proposed to generate the blur manifolds and estimate the fuzzy blur structure the pdr scheme involves the development of the meaningful cost function the estimation of blur support and structure and the optimization of the cost function experimental results show that it is effective in restoring degraded images under different environments

a new approach to constrained total least squares image restoration abstract recently there has been a growing interest and progress in using total least squares tls methods for solving blind deconvolution problems arising in image restoration here the true image is to be estimated using only partial information about the blurring operator or point spread function psf which is subject to error and noise in this paper we present a new iterative regularized and constrained tls image restoration algorithm neumann boundary conditions are used to reduce the boundary artifacts that normally occur in restoration processes preliminary numerical tests are reported on some simulated optical imaging problems in order to illustrate the effectiveness of the approach as well as the fast convergence of our iterative scheme

an adaptive servomechanism for a class of infinite dimensional systems a universal adaptive controller is constructed that achieves asymptotic tracking of a given class of reference signals and asymptotic rejection of a prescribed set of disturbance signals for a class of multivariable infinite dimensional systems that are stabilizable by high gain output feedback the controller does not require an explicit identification of the system parameters or the injection of a probing signal in contrast to most of the work in universal adaptive control this paper is based on an input output approach and the results do not require a state space representation of the plant the abstract input output results are applied to retarded systems and integrodifferential systems

second order multiobjective symmetric duality with cone constraints abstract in this paper we formulate wolfe and mond weir type second order multiobjective symmetric dual problems over arbitrary cones weak strong and converse duality theorems are established under bonvexity pseudobonvexity assumptions this work also removes several omissions in definitions models and proofs for wolfe type problems studied in mishra 9 moreover self duality theorems for these pairs are obtained assuming the function involved to be skew symmetric

iterative image restoration using approximate inverse preconditioning removing a linear shift invariant blur from a signal or image can be accomplished by inverse or wiener filtering or by an iterative least squares deblurring procedure because of the ill posed characteristics of the deconvolution problem in the presence of noise filtering methods often yield poor results on the other hand iterative methods often suffer from slow convergence at high spatial frequencies this paper concerns solving deconvolution problems for atmospherically blurred images by the preconditioned conjugate gradient algorithm where a new approximate inverse preconditioner is used to increase the rate of convergence theoretical results are established to show that fast convergence can be expected and test results are reported for a ground based astronomical imaging problem

multiobjective mixed symmetric duality involving cones a pair of multiobjective mixed symmetric dual programs is formulated over arbitrary cones weak strong converse and self duality theorems are proved for these programs under k preinvexity and k pseudoinvexity assumptions this mixed symmetric dual formulation unifies the symmetric dual formulations of suneja et al 2002 14 and khurana 2005 15

a note on pseudo invexity and symmetric duality a pair of symmetric dual nonlinear programming problems is presented and duality theorems are established under pseudoinvexity type assumptions on the kernel function this formulation removes certain inconsistencies in a recently introduced primal dual pair and gives the correct proof of various duality theorems

performance output tracking for multidimensional heat equation subject to unmatched disturbance and noncollocated control this paper investigates performance output tracking for a boundary controlled multidimensional heat equation it is assumed that the so called total disturbance which is composed of internal possibly nonlinear uncertainty and external disturbance the equation is subject to is on one part of the boundary and that the control is applied on the rest of the boundary using only partial boundary measurement we first propose an extended state observer to estimate both system state and the total disturbance this allows us to design a servomechanism and then an output feedback controller under the condition that both the reference signal and the disturbance vanish or belong to spaces h 1 0 infty l 1 gamma 1 and l 2 0 infty l 2 gamma 0 respectively we show that the over all control strategy achieves three objectives on the system performance first exponentially output tracking for arbitrary given reference signal second uniformly boundedness of all internal signals and third the internal asymptotic stability of the closed loop system in addition the control strategy turns out to be robust to the measurement noise we provide numerical experiments to illustrate the effectiveness of the proposed control strategy

symmetric duality in multiobjective programming involving generalized cone invex functions abstract in this paper cone pseudoinvex and strongly cone pseudoinvex functions are defined a pair of mond weir type symmetric dual multiobjective programs is formulated over arbitrary cones weak duality strong duality and converse duality theorems are established using the above defined functions a self duality theorem is also given by assuming the functions involved to be skew symmetric

constrained iterations for blind deconvolution and convexity issues the need for image restoration arises in many applications of various scientific disciplines such as medicine and astronomy and in general whenever an unknown image must be recovered from blurred and noisy data m bertero p boccacci introduction to inverse problems in imaging institute of physics publishing philadelphia pa usa 1998 the algorithm studied in this work restores the image without the knowledge of the blur using a little priori information and a blind inverse filter iteration it represents a variation of the methods proposed in kundur and hatzinakos a novel blind deconvolution scheme for image restoration using recursive filtering ieee trans signal process 46 2 1998 375 390 and ng et al regularization of rif blind image deconvolution ieee trans image process 9 6 2000 1130 1134 the problem of interest here is an inverse one that cannot be solved by simple filtering since it is ill posed the imaging system is assumed to be linear and space invariant this allows a simplified relationship between unknown and observed images described by a point spread function modeling the distortion the blurring though makes the restoration ill conditioned regularization is therefore also needed obtained by adding constraints to the formulation the restoration is modeled as a constrained minimization particular attention is given here to the analysis of the objective function and on establishing whether or not it is a convex function whose minima can be located by classic optimization techniques and descent methods numerical examples are applied to simulated data and to real data derived from various applications comparison with the behavior of methods d kundur d hatzinakos a novel blind deconvolution scheme for image restoration using recursive filtering ieee trans signal process 46 2 1998 375 390 and m ng r j plemmons s qiao regularization of rif blind image deconvolution ieee trans image process 9 6 2000 1130 1134 show the effectiveness of our variant

performance output tracking for a wave equation subject to unmatched general boundary harmonic disturbance in this paper we consider performance output reference tracking for a wave equation with general harmonic disturbance at one end and the control at the other end we first design a state reference system determined completely by the measured output and the reference signal where the parameter update law is presented an adaptive servomechanism output feedback control is then designed it is shown that the closed loop system is well posed three control objectives are achieved a the performance output is tracking the reference signal b all the internal loops are bounded c when the disturbance and reference are disconnected the closed loop is exponentially stable finally the state of system is shown to be tracking the reference state and the updated parameters are convergent to their true values

higher order f α ρ d convexity and symmetric duality in multiobjective programming a pair of higher order wolfe type and mond weir type multiobjective symmetric dual programs over arbitrary cones is formulated weak strong and converse duality theorems are then established using the notion of higher order f a r d convexity pseudoconvexity assumptions special cases are also discussed to show that this paper extends some known results of the literature

a new look to multichannel blind image deconvolution the aim of this paper is to propose a new look to mbid examine some known approaches and provide a new mc method for restoring blurred and noisy images first the direct image restoration problem is briefly revisited then a new method based on inverse filtering for perfect image restoration in the noiseless case is proposed the noisy case is addressed by introducing a regularization term into the objective function in order to avoid noise amplification second the filter identification problem is considered in the mc context a new robust solution to the degradation matrix filter is then derived and used in conjunction with a total variation approach to restore the original image simulation results and performance evaluations using recent image quality metrics are provided to assess the effectiveness of the proposed methods

non differentiable symmetric duality for multiobjective programming with cone constraints abstract two pairs of non differentiable multiobjective symmetric dual problems with cone constraints over arbitrary cones which are wolfe type and mond weir type are considered on the basis of weak efficiency with respect to a convex cone we obtain symmetric duality results for the two pairs of problems under cone invexity and cone pseudoinvexity assumptions on the involved functions our results extend the results in khurana s khurana symmetric duality in multiobjective programming involving generalized cone invex functions european journal of operational research 165 2005 592 597 to the non differentiable multiobjective symmetric dual problem

pseudo invexity and symmetric duality in nonlinear fractional programming abstract the purpose of this paper is to study the symmetric dual fractional programming problem for pseudo invex functions and arbitrary cones several known results are obtained as special cases

performance output exponential tracking for a wave equation with a general boundary disturbance abstract in this paper we consider performance output tracking for a wave equation with a general boundary disturbance the control and the disturbance are unmatched different from the existing results the hidden regularity instead of the high gain or variable structure is utilized in the adaptive servomechanism design as a result we are able to cope with more complicated and general disturbances moreover the performance output can track the reference signal exponentially and at the same time all the states of the subsystems involved are uniformly bounded more specially the overall closed loop system is exponentially stable when the disturbance and reference are disconnected to the system the numerical simulations are presented to illustrate the effect of the proposed scheme

tracking and disturbance rejection of mimo nonlinear systems with pi controller we study tracking and disturbance rejection of a class of mimo nonlinear systems with linear proportional plus integral pi compensator roughly speaking we show that if the given nonlinear plant is exponentially stable and has a strictly increasing dc steady state i o map then a simple pi compensator can be used to yield a stable unity feedback closed loop system which asymptotically tracks reference inputs that tend to constant vectors and asymptotically rejects disturbances that tend to constant vectors

anti collocated observer based output feedback control of wave equation with cubic velocity nonlinear boundary and dirichlet control this study focuses on the observer and observer based output feedback control design for a one dimensional wave equation with van der pol type nonlinear boundary condition and dirichlet boundary co

technical note on symmetric duality in multiobjective programming some remarks on recent results abstract certain shortcomings are pointed out in some recent works of das and nanda european journal of operational research 97 1997 167 171 and kim et al european journal of operational research 107 1998 686 691 which show that these studies are highly restricted

a regularization approach to joint blur identification and image restoration the primary difficulty with blind image restoration or joint blur identification and image restoration is insufficient information this calls for proper incorporation of a priori knowledge about the image and the point spread function psf a well known space adaptive regularization method for image restoration is extended to address this problem this new method effectively utilizes among others the piecewise smoothness of both the image and the psf it attempts to minimize a cost function consisting of a restoration error measure and two regularization terms one for the image and the other for the blur subject to other hard constraints a scale problem inherent to the cost function is identified which if not properly treated may hinder the minimization blind restoration process alternating minimization is proposed to solve this problem so that algorithmic efficiency as well as simplicity is significantly increased two implementations of alternating minimization based on steepest descent and conjugate gradient methods are presented good performance is observed with numerically and photographically blurred images even though no stringent assumptions about the structure of the underlying blur operator is made

uniform boundary controllability of a discrete 1 d wave equation a numerical scheme for the controlled discrete 1 d wave equation is considered we prove the convergence of the boundary controls of the discrete equations to a control of the continuous wave equation when the mesh size tends to zero when time and space steps coincide this positive result is in contrast with previous negative ones for space semi discretizations

a novel blind deconvolution scheme for image restoration using recursive filtering we present a novel blind deconvolution technique for the restoration of linearly degraded images without explicit knowledge of either the original image or the point spread function the technique applies to situations in which the scene consists of a finite support object against a uniformly black grey or white background this occurs in certain types of astronomical imaging medical imaging and one dimensional 1 d gamma ray spectra processing among others the only information required are the nonnegativity of the true image and the support size of the original object the restoration procedure involves recursive filtering of the blurred image to minimize a convex cost function we prove convexity of the cost function establish sufficient conditions to guarantee a unique solution and examine the performance of the technique in the presence of noise the new approach is experimentally shown to be more reliable and to have faster convergence than existing nonparametric finite support blind deconvolution methods for situations in which the exact object support is unknown we propose a novel support finding algorithm

multiobjective symmetric duality with cone constraints we formulate a pair of multiobjective symmetric dual programs for pseudo invex functions and arbitrary cones our model is unifying the wolfe vector symmetric dual and the mond weir vector symmetric dual models we establish the weak strong converse and self duality theorems for our pair of dual models nanda and das results optimization 28 1994 267 eur j oper res 88 1996 572 are obtained as special cases

multiobjective higher order symmetric duality involving generalized cone invex functions in this paper a pair of mond weir type multiobjective higher order symmetric dual programs over arbitrary cones is formulated and usual duality results are established under higher order k preinvexity k pseudoinvexity assumptions symmetric minimax mixed integer primal and dual problems are also discussed

blind deconvolution using generalized cross validation approach to regularization parameter estimation in this paper we propose and present an algorithm for total variation tv based blind deconvolution both the unknown image and blur can be estimated within an alternating minimization framework with the generalized cross validation gcv method the regularization parameters associated with the unknown image and blur can be updated in alternating minimization steps experimental results confirm that the performance of the proposed algorithm is better than variational bayesian blind deconvolution algorithms with student s t priors or a total variation prior

convergence of the alternating minimization algorithm for blind deconvolution abstract blind deconvolution refers to the image processing task of restoring the original image from a blurred version without the knowledge of the blurring function one approach that has been proposed recently t chan c wong ieee trans image process 7 1998 370 375 y you m kaveh ieee trans image process 5 1996 416 428 is a joint minimization model in which an objective function is set up consisting of three terms the data fitting term and the regularization terms for the image and the blur this model implicitly defines a one parameter family of blurred images and point spread functions psfs from which the user can decide usually using additional information which is the best restored image to find a local minimum of the objective function we use an alternating minimization am procedure y you m kaveh ieee trans image process 5 1996 416 428 in which we fix either the blur or the image and minimize respect to the other variable each step of which is a standard non blind deconvolution problem while the model is not convex and thus allows multiple solutions we have found that the am procedure always converges globally but with the converged solution depending on the initial guess in this paper we give an analysis of the am procedure which explains the convergence behavior and the observed robustness of the method

multiobjective second order mixed symmetric duality with a square root term abstract in this paper we have formulated a second order mixed symmetric dual programs for a class of nondifferentiable multiobjective programming problem weak strong and converse duality theorems are then proved for the aforementioned pair using the notion of second order f convexity pseudoconvexity assumptions further special cases are discussed to show that this paper extends some known results of the literature

a recursive soft decision approach to blind image deconvolution this paper presents a new approach to blind image deconvolution based on soft decision blur identification and hierarchical neural networks traditional blind algorithms require a hard decision on whether the blur satisfies a parametric form before their formulations as the blurring function is usually unknown a priori this precondition inhibits the incorporation of parametric blur knowledge domain into the restoration schemes the new technique addresses this difficulty by providing a continual soft decision blur adaptation with respect to the best fit parametric structure throughout deconvolution the approach integrates the knowledge of well known blur models without compromising its flexibility in restoring images degraded by nonstandard blurs an optimization scheme is developed where a new cost function is projected and minimized with respect to the image and blur domains a nested neural network called the hierarchical cluster model is employed to provide an adaptive perception based restoration its sparse synaptic connections are instrumental in reducing the computational cost of restoration conjugate gradient optimization is adopted to identify the blur due to its computational efficiency the approach is shown experimentally to be effective in restoring images degraded by different blurs

space variant restoration of images degraded by camera motion blur we examine the problem of restoration from multiple images degraded by camera motion blur we consider scenes with significant depth variations resulting in space variant blur the proposed algorithm can be applied if the camera moves along an arbitrary curve parallel to the image plane without any rotations the knowledge of camera trajectory and camera parameters is not necessary at the input the user selects a region where depth variations are negligible the algorithm belongs to the group of variational methods that estimate simultaneously a sharp image and a depth map based on the minimization of a cost functional to initialize the minimization it uses an auxiliary window based depth estimation algorithm feasibility of the algorithm is demonstrated by three experiments with real images