an extended car following model considering multi anticipative average velocity effect under v2v environment abstract vehicle to vehicle for short v2v communication technology is regarded as a promising technology to improve traffic efficiency and safety in this paper an extended car following model is proposed to simulate traffic flow by considering multi anticipative average velocity effect including the average velocity and the mean expected velocity field effect of preceding vehicles group under v2v environment the stability condition of this model is obtained by applying the linear stability theory the phase diagram comparison and analysis shows that the multi anticipative average velocity effect can effectively enhance the stabilization of traffic system in particular the average velocity effect plays a more important role than that of the mean expected velocity field effect in improving the stability of traffic flow the mkdv equation is derived to describe the evolution characteristics of traffic density waves by using the reductive perturbation method furthermore the numerical simulation is carried out to validate the theoretical results and indicates that the traffic jam can be suppressed efficiently via taking into account multi anticipative average velocity effect

two direction nonlocal model for image denoising similarities inherent in natural images have been widely exploited for image denoising and other applications in fact if a cluster of similar image patches is rearranged into a matrix similarities exist both between columns and rows using the similarities we present a two directional nonlocal tdnl variational model for image denoising the solution of our model consists of three components one component is a scaled version of the original observed image and the other two components are obtained by utilizing the similarities specifically by using the similarity between columns we get a nonlocal means like estimation of the patch with consideration to all similar patches while the weights are not the pairwise similarities but a set of clusterwise coefficients moreover by using the similarity between rows we also get nonlocal autoregression like estimations for the center pixels of the similar patches the tdnl model leads to an alternative minimization algorithm experiments indicate that the model can perform on par with or better than the state of the art denoising methods

an image multiresolution representation for lossless and lossy compression we propose a new image multiresolution transform that is suited for both lossless reversible and lossy compression the new transformation is similar to the subband decomposition but can be computed with only integer addition and bit shift operations during its calculation the number of bits required to represent the transformed image is kept small through careful scaling and truncations numerical results show that the entropy obtained with the new transform is smaller than that obtained with predictive coding of similar complexity in addition we propose entropy coding methods that exploit the multiresolution structure and can efficiently compress the transformed image for progressive transmission up to exact recovery the lossless compression ratios are among the best in the literature and simultaneously the rate versus distortion performance is comparable to those of the most efficient lossy compression methods

regularity preserving image interpolation assumptions about image continuity lead to oversmoothed edges in common image interpolation algorithms a wavelet based interpolation method that imposes no continuity constraints is introduced the algorithm estimates the regularity of edges by measuring the decay of wavelet transform coefficients across scales and preserves the underlying regularity by extrapolating a new subband to be used in image resynthesis the algorithm produces visibly sharper edges than traditional techniques and exhibits an average peak signal to noise ratio psnr improvement of 2 5 db over bilinear and bicubic techniques

an extended continuum traffic model with the consideration of the optimal velocity difference abstract in this letter we derived an extended continuum model from a car following model with consideration of the optimal velocity difference called the relative optimal velocity via employing the transformation relation from microscopic variables to macroscopic ones the stability condition of this continuum traffic model is obtained by performing linear stability analysis results show that the optimal velocity difference helps to improve the stability of traffic flow we make use of the upwind finite difference scheme for simulation the effects of the optimal velocity difference the relative optimal velocity and the velocity difference the optimal velocity on local clustering effect and instability are studied and compared each other respectively the spatiotemporal evolution patterns of traffic flow for the different initial density strength of the optimal velocity difference and the velocity difference are obtained their space time diagrams reveal the local clustering effect induced by the instability of traffic flow and generate the stop go traffic jams numerical simulation result indicates the unstable region is shrunken under the effect of the optimal velocity difference the relative optimal velocity and or the velocity difference the optimal velocity

on the role of exponential splines in image interpolation a sobolev reproducing kernel hilbert space approach to image interpolation is introduced the underlying kernels are exponential functions and are related to stochastic autoregressive image modeling the corresponding image interpolants can be implemented effectively using compactly supported exponential b splines a tight l2 upper bound on the interpolation error is then derived suggesting that the proposed exponential functions are optimal in this regard experimental results indicate that the proposed interpolation approach with properly tuned signal dependent weights outperforms currently available polynomial b spline models of comparable order furthermore a unified approach to image interpolation by ideal and nonideal sampling procedures is derived suggesting that the proposed exponential kernels may have a significant role in image modeling as well our conclusion is that the proposed sobolev based approach could be instrumental and a preferred alternative in many interpolation tasks

subpixel edge localization and the interpolation of still images in this paper we present a nonlinear interpolation scheme for still image resolution enhancement the algorithm is based on a source model emphasizing the visual integrity of detected edges and incorporates a novel edge fitting operator that has been developed for this application a small neighborhood about each pixel in the low resolution image is first mapped to a best fit continuous space step edge the bilevel approximation serves as a local template on which the higher resolution sampling grid can then be superimposed where disputed values in regions of local window overlap are averaged to smooth errors the result is an image of increased resolution with noticeably sharper edges and in all tried cases lower mean squared reconstruction error than that produced by linear techniques

progressive image denoising through hybrid graph laplacian regularization a unified framework recovering images from corrupted observations is necessary for many real world applications in this paper we propose a unified framework to perform progressive image recovery based on hybrid graph laplacian regularized regression we first construct a multiscale representation of the target image by laplacian pyramid then progressively recover the degraded image in the scale space from coarse to fine so that the sharp edges and texture can be eventually recovered on one hand within each scale a graph laplacian regularization model represented by implicit kernel is learned which simultaneously minimizes the least square error on the measured samples and preserves the geometrical structure of the image data space in this procedure the intrinsic manifold structure is explicitly considered using both measured and unmeasured samples and the nonlocal self similarity property is utilized as a fruitful resource for abstracting a priori knowledge of the images on the other hand between two successive scales the proposed model is extended to a projected high dimensional feature space through explicit kernel mapping to describe the interscale correlation in which the local structure regularity is learned and propagated from coarser to finer scales in this way the proposed algorithm gradually recovers more and more image details and edges which could not been recovered in previous scale we test our algorithm on one typical image recovery task impulse noise removal experimental results on benchmark test images demonstrate that the proposed method achieves better performance than state of the art algorithms

analysis of car following model considering driver s physical delay in sensing headway abstract an extended car following model is proposed by taking into account the delay of the driver s response in sensing headway the stability condition of this model is obtained by using the linear stability theory the results show that the stability region decreases when the driver s physical delay in sensing headway increases the kdv equation and mkdv equation near the neutral stability line and the critical point are respectively derived by applying the reductive perturbation method the traffic jams could be thus described by soliton solution and kink antikink soliton solution for the kdv equation and mkdv equation respectively the numerical results in the form of the space time evolution of headway show that the stabilization effect is weakened when the driver s physical delay increases it confirms the fact that the delay of driver s response in sensing headway plays an important role in jamming transition and the numerical results are in good agreement with the theoretical analysis

robust soft decision interpolation using weighted least squares soft decision adaptive interpolation sai provides a powerful framework for image interpolation the robustness of sai can be further improved by using weighted least squares estimation instead of least squares estimation in both of the parameter estimation and data estimation steps to address the mismatch issue of geometric duality during parameter estimation the residuals prediction errors are weighted according to the geometric similarity between the pixel of interest and the residuals the robustness of data estimation can be improved by modeling the weights of residuals with the well known bilateral filter experimental results show that there is a 0 25 db increase in peak signal to noise ratio psnr for a sample set of natural images after the suggested improvements are incorporated into the original sai the proposed algorithm produces the highest quality in terms of psnr and subjective quality among sophisticated algorithms in the literature

rewiring filterbanks for local fourier analysis theory and practice this paper describes a series of new results outlining equivalences between certain rewirings of filterbank system block diagrams and the corresponding actions of convolution modulation and downsampling operators this gives rise to a general framework of reverse order and convolution subband structures in filterbank transforms which we show to be well suited to the analysis of filterbank coefficients arising from subsampled or multiplexed signals these results thus provide a means to understand time localized aliasing and modulation properties of such signals and their subband representations notions that are notably absent from the global viewpoint afforded by fourier analysis as well as signal recovery from sampled sequences based on their filterbank characterizations the utility of filterbank rewirings is demonstrated by the closed form analysis of signals subject to degradations such as missing data spatially or temporally multiplexed data acquisition or signal dependent noise the likes of which are often encountered in practical signal processing applications

kdv and kink antikink solitons in car following models abstract the jams in the congested traffic are related with various density waves which might be governed by the nonlinear wave equations such as the korteweg de vries kdv equation the burgers equation and the modified korteweg de vries mkdv equation three different versions of optimal velocity models are examined the stability conditions of the models are obtained by using the linear stability theory the kdv equation near the neutral stability line and the mkdv equation around the critical point are derived by applying the reductive perturbation method respectively the traffic jams could be thus described by the kdv and kink antikink soliton solutions for the two kinds of equations the general solutions are given for which can lead to specific solutions in previous work moreover they are applied to solve a new model the full velocity difference model and the corresponding kdv and kink antikink soliton solutions could be quickly obtained which demonstrates the general solutions presented herein are useful

modeling and application analysis of car following model with predictive headway variation abstract this work proposes an improved microscopic model by taking the predictive driving behavior of the preceding car into account firstly we use the linear method to investigate performance of the proposed model deduce the critical linear stability conditions and obtain a neutral curve accounting for stability of the new model then using the nonlinear analysis technique we derive the equations of burgers the korteweg de vries kdv and the modified kdv mkdv respectively to describe the triangular shock wave soliton density wave and the kink antikink density wave finally numerical experiments unfold from two aspects one for verifying the theoretical analysis results and another for testing the impact of predictive driving behavior of the preceding car on traffic stability simulation results show that the new model can stabilize traffic flow compared with the fvd model and confirm that the existence of three kinds of density waves in different phase diagram regions

an extended macro traffic flow model accounting for the driver s bounded rationality and numerical tests abstract in this paper we propose a macro traffic flow model to explore the effects of the driver s bounded rationality on the evolutions of traffic waves which include shock and rarefaction waves and small perturbation and on the fuel consumption and emissions that include co hc and no x during the evolution process the numerical results illustrate that considering the driver s bounded rationality can prominently smooth the wavefront of the traffic waves and improve the stability of traffic flow which shows that the driver s bounded rationality has positive impacts on traffic flow but considering the driver s bounded rationality reduces the fuel consumption and emissions only at the upstream of the rarefaction wave while enhances the fuel consumption and emissions under other situations which shows that the driver s bounded rationality has positive impacts on the fuel consumption and emissions only at the upstream of the rarefaction wave while negative effects on the fuel consumption and emissions under other situations in addition the numerical results show that the driver s bounded rationality has little prominent impact on the total fuel consumption and emissions during the whole evolution of small perturbation

single image super resolution using local geometric duality and non local similarity super resolution sr from a single image plays an important role in many computer vision applications it aims to estimate a high resolution hr image from an input low resolution lr image to ensure a reliable and robust estimation of the hr image we propose a novel single image sr method that exploits both the local geometric duality gd and the non local similarity of images the main principle is to formulate these two typically existing features of images as effective priors to constrain the super resolved results in consideration of this principle the robust soft decision interpolation method is generalized as an outstanding adaptive gd agd based local prior to adaptively design weights for the agd prior a local non smoothness detection method and a directional standard deviation based weights selection method are proposed after that the agd prior is combined with a variational framework based non local prior furthermore the proposed algorithm is speeded up by a fast gd matrices construction method which primarily relies on the selective pixel processing the extensive experimental results verify the effectiveness of the proposed method compared with several state of the art sr algorithms

fast image interpolation via random forests this paper proposes a two stage framework for fast image interpolation via random forests firf the proposed firf method gives high accuracy as well as requires low computation the underlying idea of this proposed work is to apply random forests to classify the natural image patch space into numerous subspaces and learn a linear regression model for each subspace to map the low resolution image patch to high resolution image patch the firf framework consists of two stages stage 1 of the framework removes most of the ringing and aliasing artifacts in the initial bicubic interpolated image while stage 2 further refines the stage 1 interpolated image by varying the number of decision trees in the random forests and the number of stages applied the proposed firf method can realize computationally scalable image interpolation extensive experimental results show that the proposed firf 3 2 method achieves more than 0 3 db improvement in peak signal to noise ratio over the state of the art nonlocal autoregressive modeling narm method moreover the proposed firf 1 1 obtains similar or better results as narm while only takes its 0 3 computational time

segment adaptive gradient angle interpolation we introduce a new edge directed interpolator based on locally defined straight line approximations of image isophotes spatial derivatives of image intensity are used to describe the principal behavior of pixel intersecting isophotes in terms of their slopes the slopes are determined by inverting a tridiagonal matrix and are forced to vary linearly from pixel to pixel within segments image resizing is performed by interpolating along the approximated isophotes the proposed method can accommodate arbitrary scaling factors provides state of the art results in terms of psnr as well as other quantitative visual quality metrics and has the advantage of reduced computational complexity that is directly proportional to the number of pixels

a new car following model for autonomous vehicles flow with mean expected velocity field abstract due to the development of the modern scientific technology autonomous vehicles may realize to connect with each other and share the information collected from each vehicle an improved forward considering car following model was proposed with mean expected velocity field to describe the autonomous vehicles flow behavior the new model has three key parameters adjustable sensitivity strength factor and mean expected velocity field size two lemmas and one theorem were proven as criteria for judging the stability of homogeneousautonomous vehicles flow theoretical results show that the greater parameters means larger stability regions a series of numerical simulations were carried out to check the stability and fundamental diagram of autonomous flow from the numerical simulation results the profiles hysteresis loop and density waves of the autonomous vehicles flow were exhibited the results show that with increased sensitivity strength factor or field size the traffic jam was suppressed effectively which are well in accordance with the theoretical results moreover the fundamental diagrams corresponding to three parameters respectively were obtained it demonstrates that these parameters play almost the same role on traffic flux i e before the critical density the bigger parameter is the greater flux is and after the critical density the opposite tendency is in general the three parameters have a great influence on the stability and jam state of the autonomous vehicles flow

fast image upsampling via the displacement field in this paper we present a fast image upsampling method within a two scale framework to ensure the sharp construction of upsampled image for both large scale edges and small scale structures in our approach the low frequency image is recovered via a novel sharpness preserving interpolation technique based on a well constructed displacement field which is estimated by a cross resolution sharpness preserving model within this model the distances of pixels on edges are preserved which enables the recovery of sharp edges in the high resolution result likewise local high frequency structures are reconstructed via a sharpness preserving reconstruction algorithm extensive experiments show that our method outperforms current state of the art approaches based on quantitative and qualitative evaluations as well as perceptual evaluation by a user study moreover our approach is very fast so as to be practical for real applications

edge direction preserving image zooming a mathematical and numerical analysis we focus in this paper on some reconstruction restoration methods whose aim is to improve the resolution of digital images the main point here is to study the ability of such methods to preserve one dimensional 1d structures indeed such structures are important since they are often carried by the image edges first we focus on linear methods give a general framework to design them and show that the preservation of 1d structures pleads in favor of the cancellation of the periodization of the image spectrum more precisely we show that preserving 1d structures implies the linear methods to be written as a convolution of the sinc interpolation as a consequence we cannot cope linearly with gibbs effects sharpness of the results and the preservation of the 1d structure second we study variational nonlinear methods and in particular the one based on total variation we show that this latter permits us to avoid these shortcomings we also prove the existence and consistency of an approximate solution to this variational problem at last this theoretical study is highlighted by experiments both on synthetic and natural images which show the effects of the described methods on images as well as on their spectrum

optimized compact support interpolation kernels in this paper we investigate the problem of designing compact support interpolation kernels for a given class of signals by using calculus of variations we simplify the optimization problem from an nonlinear infinite dimensional problem to a linear finite dimensional case and then find the optimum compact support function that best approximates a given filter in the least square sense l2 norm the benefit of compact support interpolants is the low computational complexity in the interpolation process while the optimum compact support interpolant guarantees the highest achievable signal to noise ratio snr our simulation results confirm the superior performance of the proposed kernel compared to other conventional compact support interpolants such as cubic spline

proportional differential effects in traffic car following model system abstract traffic flow system s stability analysis can be seen as a typical control system problem which has three kinds of damping ratio properties from the viewpoint of control theory to discover the damping ratio characteristics of traffic flow system under sudden outer impulse firstly we proposed a new method to build the traffic flow model based on state space theory secondly we deduced one form of state transfer matrix from system closed loop transfer function reversely lastly we fully investigated the proportional differential effects in the traffic car following model system analytically and numerically for unsteady traffic flow system with the proportional differential pd term in order to suppress the traffic jam we derived a kind of decoupled delay feedback control algorithm which used h norm and the numerical simulation method to find the feedback gain k and delay time the traffic flow system s transient performance formula was presented with and without pd term and we found out the important impact of pd term on traffic flow system stability the numerical simulation results substantiate our theoretical and analytical results

image interpolation by adaptive 2 d autoregressive modeling and soft decision estimation the challenge of image interpolation is to preserve spatial details we propose a soft decision interpolation technique that estimates missing pixels in groups rather than one at a time the new technique learns and adapts to varying scene structures using a 2 d piecewise autoregressive model the model parameters are estimated in a moving window in the input low resolution image the pixel structure dictated by the learnt model is enforced by the soft decision estimation process onto a block of pixels including both observed and estimated the result is equivalent to that of a high order adaptive nonseparable 2 d interpolation filter this new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods and it produces the best results so far over a wide range of scenes in both psnr measure and subjective visual quality edges and textures are well preserved and common interpolation artifacts blurring ringing jaggies zippering etc are greatly reduced

traffic behavior of mixed traffic flow with two kinds of different self stabilizing control vehicles abstract in this paper we propose a heterogeneous car following model in terms of an extension to the original optimal velocity model characterizing two classes of different self stabilizing control vehicles linear stability analysis method is utilized to the extended model for purpose to explore how the varying percentages of the vehicles with short duration self stabilizing control influence the stability of the heterogeneous traffic flow we obtain the neutral stability lines for different percentages of two classes of vehicles with finding that the traffic flow trends to stable with the decrease of the percentage for short duration self stabilizing control vehicles moreover we explore a special case that the same numbers of two different classes of vehicles with self stabilizing control we theoretically derive the stability condition of the special case and conclude the effect of the average value and the standard deviation of two time gaps on the heterogeneous traffic stability at last direct simulations are conducted to verify the conclusion of theoretical analysis

image interpolation via regularized local linear regression the linear regression model is a very attractive tool to design effective image interpolation schemes some regression based image interpolation algorithms have been proposed in the literature in which the objective functions are optimized by ordinary least squares ols however it is shown that interpolation with ols may have some undesirable properties from a robustness point of view even small amounts of outliers can dramatically affect the estimates to address these issues in this paper we propose a novel image interpolation algorithm based on regularized local linear regression rllr starting with the linear regression model where we replace the ols error norm with the moving least squares mls error norm leads to a robust estimator of local image structure to keep the solution stable and avoid overfitting we incorporate the l2 norm as the estimator complexity penalty moreover motivated by recent progress on manifold based semi supervised learning we explicitly consider the intrinsic manifold structure by making use of both measured and unmeasured data points specifically our framework incorporates the geometric structure of the marginal probability distribution induced by unmeasured samples as an additional local smoothness preserving constraint the optimal model parameters can be obtained with a closed form solution by solving a convex optimization problem experimental results on benchmark test images demonstrate that the proposed method achieves very competitive performance with the state of the art interpolation algorithms especially in image edge structure preservation

theoretical analysis of the density wave in a new continuum model and numerical simulation abstract considered the effect of traffic anticipation in the real world a new anticipation driving car following model ad cf was proposed by zheng et al based on ad cf model adopted an asymptotic approximation between the headway and density a new continuum model is presented in this paper the neutral stability condition is obtained by applying the linear stability theory additionally the korteweg de vries kdv equation is derived via nonlinear analysis to describe the propagating behavior of traffic density wave near the neutral stability line the numerical simulation and the analytical results show that the new continuum model is capable of explaining some particular traffic phenomena

solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity a general framework for solving image inverse problems with piecewise linear estimations is introduced in this paper the approach is based on gaussian mixture models which are estimated via a maximum a posteriori expectation maximization algorithm a dual mathematical interpretation of the proposed framework with a structured sparse estimation is described which shows that the resulting piecewise linear estimate stabilizes the estimation when compared with traditional sparse inverse problem techniques we demonstrate that in a number of image inverse problems including interpolation zooming and deblurring of narrow kernels the same simple and computationally efficient algorithm yields results in the same ballpark as that of the state of the art

a new lattice model accounting for multiple optimal current differences anticipation effect in two lane system abstract this paper extends a two lane lattice hydrodynamic traffic flow model to take into account the driver s anticipation effect in sensing the multiple optimal current differences based on the proposed model we derive analytically the effect of driver s anticipation of multiple optimal current differences on the instability of traffic dynamics the phase diagrams have been plotted and discussed that the stability region enhances with anticipation effect in sensing multiple optimal current differences through simulation it is found that the oscillation of density wave around critical density decreases with an increase in lattice number and anticipation time for transient and steady state the simulation results are in good agreement with the theoretical analysis which show that considering the driver s anticipation of multiple optimal current differences in two lane lattice model stabilizes the traffic flow and suppresses the traffic jam efficiently

image interpolation via graph based bayesian label propagation in this paper we propose a novel image interpolation algorithm via graph based bayesian label propagation the basic idea is to first create a graph with known and unknown pixels as vertices and with edge weights encoding the similarity between vertices then the problem of interpolation converts to how to effectively propagate the label information from known points to unknown ones this process can be posed as a bayesian inference in which we try to combine the principles of local adaptation and global consistency to obtain accurate and robust estimation specially our algorithm first constructs a set of local interpolation models which predict the intensity labels of all image samples and a loss term will be minimized to keep the predicted labels of the available low resolution lr samples sufficiently close to the original ones then all of the losses evaluated in local neighborhoods are accumulated together to measure the global consistency on all samples moreover a graph laplacian based manifold regularization term is incorporated to penalize the global smoothness of intensity labels such smoothing can alleviate the insufficient training of the local models and make them more robust finally we construct a unified objective function to combine together the global loss of the locally linear regression square error of prediction bias on the available lr samples and the manifold regularization term it can be solved with a closed form solution as a convex optimization problem experimental results demonstrate that the proposed method achieves competitive performance with the state of the art image interpolation algorithms

nonlocal image restoration with bilateral variance estimation a low rank approach simultaneous sparse coding ssc or nonlocal image representation has shown great potential in various low level vision tasks leading to several state of the art image restoration techniques including bm3d and lssc however it still lacks a physically plausible explanation about why ssc is a better model than conventional sparse coding for the class of natural images meanwhile the problem of sparsity optimization especially when tangled with dictionary learning is computationally difficult to solve in this paper we take a low rank approach toward ssc and provide a conceptually simple interpretation from a bilateral variance estimation perspective namely that singular value decomposition of similar packed patches can be viewed as pooling both local and nonlocal information for estimating signal variances such perspective inspires us to develop a new class of image restoration algorithms called spatially adaptive iterative singular value thresholding saist for noise data saist generalizes the celebrated bayesshrink from local to nonlocal models for incomplete data saist extends previous deterministic annealing based solution to sparsity optimization through incorporating the idea of dictionary learning in addition to conceptual simplicity and computational efficiency saist has achieved highly competent often better objective performance compared to several state of the art methods in image denoising and completion experiments our subjective quality results compare favorably with those obtained by existing techniques especially at high noise levels and with a large amount of missing data

a stabilized finite element method using a discontinuous level set approach for the computation of bubble dynamics a novel numerical method for solving three dimensional two phase flow problems is presented this method combines a quadrature free discontinuous galerkin method for the level set equation with a pressure stabilized finite element method for the navier stokes equations the main challenge in the computation of such flows is the accurate evaluation of surface tension forces this involves the computation of the curvature of the fluid interface in the context of the discontinuous galerkin method we show that the use of a curvature computed by means of a direct derivation of the level set function leads to inaccurate and oscillatory results a more robust second order least squares computation of the curvature that filters out the high frequencies and produces converged results is presented this whole numerical technology allows to simulate a wide range of flow regimes with large density ratios to accurately capture the shape of the deforming interface of the bubble and to maintain good mass conservation

adaptive image resizing based on continuous domain stochastic modeling we introduce an adaptive continuous domain modeling approach to texture and natural images the continuous domain image is assumed to be a smooth function and we embed it in a parameterized sobolev space we point out a link between sobolev spaces and stochastic auto regressive models and exploit it for optimally choosing sobolev parameters from available pixel values to this aim we use exact continuous to discrete mapping of the auto regressive model that is based on symmetric exponential splines the mapping is computationally efficient and we exploit it for maximizing an approximated gaussian likelihood function we account for non gaussian levy type processes by deriving a more robust estimator that is based on the sample auto correlation sequence both estimators use multiple initialization values for overcoming the local minima structure of the fitting criteria experimental image resizing results indicate that the auto correlation criterion can cope better with non gaussian processes and model mismatch our work demonstrates the importance of the auto correlation function in adaptive image interpolation and image modeling tasks and we believe it is instrumental in other image processing tasks as well

stabilization and enhancement of traffic flow by the next nearest neighbor interaction the car following model of traffic is extended to take into account the car interaction before the next car ahead the next nearest neighbor interaction the traffic behavior of the extended car following model is investigated numerically and analytically it is shown that the next nearest neighbor interaction stabilizes the traffic flow the jamming transition between the freely moving and jammed phases occurs at a higher density than the threshold of the original car following model by increasing the maximal velocity the traffic current is enhanced without jam by the stabilization effect the jamming transition is analyzed with the use of the linear stability and nonlinear perturbation methods the traffic jam is described by the kink solution of the modified korteweg char21 de vries equation the theoretical coexisting curve is in good agreement with the simulation result

stabilization analysis and modified korteweg de vries equation in a cooperative driving system two lattice traffic models are proposed by incorporating a cooperative driving system the lattice versions of the hydrodynamic model of traffic flow are described by the differential difference equation and difference difference equation respectively the stability conditions for the two models are obtained using the linear stability theory the results show that considering more than one site ahead in vehicle motion leads to the stabilization of the system the modified korteweg de vries equation the mkdv equation for short near the critical point is derived by using the reductive perturbation method to show the traffic jam which is proved to be described by kink anti kink soliton solutions obtained from the mkdv equations

the backward looking effect in the lattice hydrodynamic model abstract the novel lattice hydrodynamic model is presented by incorporating the backward looking effect the stability condition for the the model is obtained using the linear stability theory the result shows that considering one following site in vehicle motion leads to the stabilization of the system compared with the original lattice hydrodynamic model and the cooperative driving lattice hydrodynamic model the korteweg de vries kdv for short equation near the neutral stability line is derived by using the reductive perturbation method to show the traffic jam which is proved to be described by kdv soliton solution obtained from the kdv equation the simulation result is consistent with the nonlinear analysis

multiscale semilocal interpolation with antialiasing aliasing is a common artifact in low resolution lr images generated by a downsampling process recovering the original high resolution image from its lr counterpart while at the same time removing the aliasing artifacts is a challenging image interpolation problem since a natural image normally contains redundant similar patches the values of missing pixels can be available at texture relevant lr pixels based on this we propose an iterative multiscale semilocal interpolation method that can effectively address the aliasing problem the proposed method estimates each missing pixel from a set of texture relevant semilocal lr pixels with the texture similarity iteratively measured from a sequence of patches of varying sizes specifically in each iteration top texture relevant lr pixels are used to construct a data fidelity term in a maximum a posteriori estimation and a bilateral total variation is used as the regularization term experimental results compared with existing interpolation methods demonstrate that our method can not only substantially alleviate the aliasing problem but also produce better results across a wide range of scenes both in terms of quantitative evaluation and subjective visual quality

a motion aligned auto regressive model for frame rate up conversion in this paper a motion aligned auto regressive maar model is proposed for frame rate up conversion where each pixel is interpolated as the average of the results generated by one forward maar fw maar model and one backward maar bw maar model in the fw maar model each pixel in the to be interpolated frame is generated as a linear weighted summation of the pixels within a motion aligned square neighborhood in the previous frame to derive more accurate interpolation weights the aligned actual pixels in the following frame are also estimated as a linear weighted summation of the newly interpolated pixels in the to be interpolated frame by the same weights consequently the backward aligned actual pixels in the following frame can be estimated as a weighted summation of the corresponding pixels within an enlarged square neighborhood in the previous frame the bw maar is performed likewise except that it is operated in the reverse direction a damping newton algorithm is then proposed to compute the adaptive interpolation weights for the fw maar and bw maar models extensive experiments demonstrate that the proposed maar model is able to achieve superior performance than the traditional frame interpolation methods such as mci obmc and aobmc and it is even better than star model for the most test sequences with moderate or large motions

single image superresolution based on gradient profile sharpness single image superresolution is a classic and active image processing problem which aims to generate a high resolution hr image from a low resolution input image due to the severely under determined nature of this problem an effective image prior is necessary to make the problem solvable and to improve the quality of generated images in this paper a novel image superresolution algorithm is proposed based on gradient profile sharpness gps gps is an edge sharpness metric which is extracted from two gradient description models i e a triangle model and a gaussian mixture model for the description of different kinds of gradient profiles then the transformation relationship of gpss in different image resolutions is studied statistically and the parameter of the relationship is estimated automatically based on the estimated gps transformation relationship two gradient profile transformation models are proposed for two profile description models which can keep profile shape and profile gradient magnitude sum consistent during profile transformation finally the target gradient field of hr image is generated from the transformed gradient profiles which is added as the image prior in hr image reconstruction model extensive experiments are conducted to evaluate the proposed algorithm in subjective visual effect objective quality and computation time the experimental results demonstrate that the proposed approach can generate superior hr images with better visual quality lower reconstruction error and acceptable computation efficiency as compared with state of the art works

global consistency local sparsity and pixel correlation a unified framework for face hallucination in this paper a novel two phase framework is presented to deal with the face hallucination problem in the first phase an initial high resolution hr face image is produced in patch wise each input low resolution lr patch is represented as a linear combination of training patches and the corresponding hr patch is estimated by the same combination coefficients realizing that training patches similar with the input may provide more appropriate textures in the reconstruction we regularize the combination coefficients by a weighted 2 norm minimization term which enlarges the coefficients for relevant patches the hr face image is then initialized by integrating all the hr patches in the second phase three regularization models are introduced to produce the final hr face image different from most previous approaches which consider global and local priors separately the proposed algorithm incorporates the global reconstruction model the local sparsity model and the pixel correlation model into a unified regularization framework initializing the regularization problem with the hr image obtained in the first phase the final output hr image can be optimized through an iterative procedure experimental results show that the proposed algorithm achieves better performances in both reconstruction error and visual quality

nonlinear image upsampling method based on radial basis function interpolation in this paper we present a novel edge directed upsampling method based on radial basis function rbf interpolation in order to remove artifacts such as blurred edges or blocking effects we suggest a nonlinear method capable of taking edge information into account the resampling evaluation is determined according to the edge orientation the proposed scheme is as simple to implement as linear methods but it demonstrates improved visual quality by preserving the edge features better than the classical linear interpolation methods the algorithm is compared with some well known linear schemes as well as recently developed nonlinear schemes the resulting images demonstrate the new algorithm s ability to magnify an image while preserving the edge features

contrast guided image interpolation in this paper a contrast guided image interpolation method is proposed that incorporates contrast information into the image interpolation process given the image under interpolation four binary contrast guided decision maps cdms are generated and used to guide the interpolation filtering through two sequential stages 1 the 45 and 135 cdms for interpolating the diagonal pixels and 2 the 0 and 90 cdms for interpolating the row and column pixels after applying edge detection to the input image the generation of a cdm lies in evaluating those nearby non edge pixels of each detected edge for re classifying them possibly as edge pixels this decision is realized by solving two generalized diffusion equations over the computed directional variation dv fields using a derived numerical approach to diffuse or spread the contrast boundaries or edges respectively the amount of diffusion or spreading is proportional to the amount of local contrast measured at each detected edge the diffused dv fields are then thresholded for yielding the binary cdms respectively therefore the decision bands with variable widths will be created on each cdm the two cdms generated in each stage will be exploited as the guidance maps to conduct the interpolation process for each declared edge pixel on the cdm a 1 d directional filtering will be applied to estimate its associated to be interpolated pixel along the direction as indicated by the respective cdm otherwise a 2 d directionless or isotropic filtering will be used instead to estimate the associated missing pixels for each declared non edge pixel extensive simulation results have clearly shown that the proposed contrast guided image interpolation is superior to other state of the art edge guided image interpolation methods in addition the computational complexity is relatively low when compared with existing methods hence it is fairly attractive for real time image applications

video compression artifact reduction via spatio temporal multi hypothesis prediction annoying compression artifacts exist in most of lossy coded videos at low bit rates which are caused by coarse quantization of transform coefficients or motion compensation from distorted frames in this paper we propose a compression artifact reduction approach that utilizes both the spatial and the temporal correlation to form multi hypothesis predictions from spatio temporal similar blocks for each transform block three predictions with their reliabilities are estimated respectively the first prediction is constructed by inversely quantizing transform coefficients directly and its reliability is determined by the variance of quantization noise the second prediction is derived by representing each transform block with a temporal auto regressive tar model along its motion trajectory and its corresponding reliability is estimated from local prediction errors of the tar model the last prediction infers the original coefficients from similar blocks in non local regions and its reliability is estimated based on the distribution of coefficients in these similar blocks finally all the predictions are adaptively fused according to their reliabilities to restore high quality videos the experimental results show that the proposed method can efficiently reduce most of the compression artifacts and improve both subjective and objective quality of block transform coded videos

graph laplacian regularization for image denoising analysis in the continuous domain inverse imaging problems are inherently underdetermined and hence it is important to employ appropriate image priors for regularization one recent popular prior the graph laplacian regularizer assumes that the target pixel patch is smooth with respect to an appropriately chosen graph however the mechanisms and implications of imposing the graph laplacian regularizer on the original inverse problem are not well understood to address this problem in this paper we interpret neighborhood graphs of pixel patches as discrete counterparts of riemannian manifolds and perform analysis in the continuous domain providing insights into several fundamental aspects of graph laplacian regularization for image denoising specifically we first show the convergence of the graph laplacian regularizer to a continuous domain functional integrating a norm measured in a locally adaptive metric space focusing on image denoising we derive an optimal metric space assuming non local self similarity of pixel patches leading to an optimal graph laplacian regularizer for denoising in the discrete domain we then interpret graph laplacian regularization as an anisotropic diffusion scheme to explain its behavior during iterations e g its tendency to promote piecewise smooth signals under certain settings to verify our analysis an iterative image denoising algorithm is developed experimental results show that our algorithm performs competitively with state of the art denoising methods such as bm3d for natural images and outperforms them significantly for piecewise smooth images

nonlinear analysis of traffic flow on a gradient highway abstract we investigate the slope effects upon traffic flow on a single lane gradient uphill downhill highway analytically and numerically the stability condition neutral stability condition and instability condition are obtained by the use of linear stability theory it is found that stability of traffic flow on the gradient varies with the slopes the burgers korteweg de vries kdv and modified korteweg de vries mkdv equations are derived to describe the triangular shock waves soliton waves and kink antikink waves in the stable meta stable and unstable region respectively a series of simulations are carried out to reproduce the triangular shock waves kink antikink waves and soliton waves results show that amplitudes of the triangular shock waves and kink antikink waves vary with the slopes the soliton wave appears in an upward form when the average headway is less than the safety distance and a downward form when the average headway is more than the safety distance moreover both the kink antikink waves and the solitary waves propagate backwards the numerical simulation shows a good agreement with the analytical result

a map based image interpolation method via viterbi decoding of markov chains of interpolation functions a new method of image resolution up conversion image interpolation based on maximum a posteriori sequence estimation is proposed instead of making a hard decision about the value of each missing pixel we estimate the missing pixels in groups at each missing pixel of the high resolution hr image we consider an ensemble of candidate interpolation methods interpolation functions the interpolation functions are interpreted as states of a markov model in other words the proposed method undergoes state transitions from one missing pixel position to the next accordingly the interpolation problem is translated to the problem of estimating the optimal sequence of interpolation functions corresponding to the sequence of missing hr pixel positions we derive a parameter free probabilistic model for this to be estimated sequence of interpolation functions then we solve the estimation problem using a trellis representation and the viterbi algorithm using directional interpolation functions and sequence estimation techniques we classify the new algorithm as an adaptive directional interpolation using soft decision estimation techniques experimental results show that the proposed algorithm yields images with higher or comparable peak signal to noise ratios compared with some benchmark interpolation methods in the literature while being efficient in terms of implementation and complexity considerations

image zooming method using edge directed moving least squares interpolation based on exponential polynomials this paper presents a nonlinear image interpolation algorithm the suggested method is based on the moving least squares mls projection technique but introduces a fundamental modification the algebraic polynomial based mls methods provide very satisfactory results however the associated approximation space is shift and scale invariant so that it cannot be adjusted according to the characteristic of a given data as a result when upsampling images it has a limitation in producing sharp edges such that edges are often blurred in the magnified images to recover sharper edges we need to reduce smoothing parameter or adapt a new parameter sharpening the edges motivated by this observations we propose a novel mls method governed by a set of exponential polynomials with tension parameters such that they can be tuned to the characteristic of given data moreover for a better match to the local structures around the edges the suggested algorithm uses weights which consider the edge orientation numerical results are presented and compared visually and by using some quantitative fidelity measures psnr epsnr ssim and fsim to the bicubic spline interpolation and other recently developed nonlinear methods the results demonstrate the new algorithm s ability to magnify an image while preserving edge features

graph based transform for 2d piecewise smooth signals with random discontinuity locations the graph based block transform recently emerged as an effective tool for compressing some special signals such as depth images in 3d videos however in existing methods overheads are required to describe the graph of the block from which the decoder has to calculate the transform via time consuming eigendecomposition to address these problems in this paper we aim to develop a single graph based transform for a class of 2d piecewise smooth signals with similar discontinuity patterns we first consider the deterministic case with a known discontinuity location in each row we propose a 2d first order autoregression 2d ar1 model and a 2d graph for this type of signals we show that the closed form expression of the inverse of a biased laplacian matrix of the proposed 2d graph is exactly the covariance matrix of the proposed 2d ar1 model therefore the optimal transform for the signal are the eigenvectors of the proposed graph laplacian next we show that similar results hold in the random case where the locations of the discontinuities in different rows are randomly distributed within a confined region and we derive the closed form expression of the corresponding optimal 2d graph laplacian the theory developed in this paper can be used to design both pre computed transforms and signal dependent transforms with low complexities finally depth image coding experiments demonstrate that our methods can achieve similar performance to the state of the art method but our complexity is much lower

selective data pruning based compression using high order edge directed interpolation this paper proposes a selective data pruning based compression scheme to improve the rate distortion relation of compressed images and video sequences the original frames are pruned to a smaller size before compression after decoding they are interpolated back to their original size by an edge directed interpolation method the data pruning phase is optimized to obtain the minimal distortion in the interpolation phase furthermore a novel high order interpolation is proposed to adapt the interpolation to several edge directions in the current frame this high order filtering uses more surrounding pixels in the frame than the fourth order edge directed method and it is more robust the algorithm is also considered for multiframe based interpolation by using spatio temporally surrounding pixels coming from the previous frame simulation results are shown for both image interpolation and coding applications to validate the effectiveness of the proposed methods

an extended car following model based on intelligent transportation system application abstract the jams in the congested traffic reveal various density waves some of them are described by the nonlinear wave equations the korteweg de vries kdv equation the burgers equation and the modified kdv equation an extended car following model are proposed in previous work and the kink antikink solution has been obtained from the mkdv equation we continue to derive the kdv equation near the neutral stability line by applying the reductive perturbation method the traffic jam could be thus described by the soliton solution and the analysis result is consistent with the previous one from the numerical simulations results the soliton waves are found and traffic jam is suppressed efficiently as encounter big disturbances

an edge guided image interpolation algorithm via directional filtering and data fusion preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high resolution image from a low resolution counterpart we propose a new edge guided nonlinear interpolation technique through directional filtering and data fusion for a pixel to be interpolated two observation sets are defined in two orthogonal directions and each set produces an estimate of the pixel value these directional estimates modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square error estimation lmmse technique into a more robust estimate using the statistics of the two observation sets we also present a simplified version of the lmmse based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts

adaptive chroma subsampling binding and luma guided chroma reconstruction method for screen content images in this paper we propose a novel adaptive chroma subsampling binding and luma guided asblg chroma reconstruction method for screen content images scis after receiving the decoded luma and subsampled chroma image from the decoder a fast winner first voting strategy is proposed to identify the used chroma subsampling scheme prior to compression then the decoded luma image is subsampled as the identified subsampling scheme was performed on the chroma image such that we are able to conclude an accurate correlation between the subsampled decoded luma image and the decoded subsampled chroma image accordingly an adaptive sliding window based and luma guided chroma reconstruction method is proposed the related computational complexity analysis is also provided we take two quality metrics the color peak signal to noise ratio cpsnr of the reconstructed chroma images and scis and the gradient based structure similarity index cgss of the reconstructed scis to evaluate the quality performance let the proposed chroma reconstruction method be denoted as asblg based on 26 typical test scis and 6 jct vc test screen content video sequences scvs several experiments show that on average the cpsnr gains of all the reconstructed uv images by 4 2 0 a asblg scis by 4 2 0 mpeg b asblg and scvs by 4 2 0 a asblg are 2 1 1 87 and 1 87 db respectively when compared with that of the other combinations specifically in terms of cpsnr and cgss csbilinear asblg for the test scis and csbicubic asblg for the test scvs outperform the existing state of the art comparative combinations where csbilinear and csbicubic denote the luma aware based chroma subsampling schemes by wang et al

ell_ 2 restoration of ell_ infty decoded images via soft decision estimation the l constrained image coding is a technique to achieve substantially lower bit rate than strictly mathematically lossless image coding while still imposing a tight error bound at each pixel however this technique becomes inferior in the l2 distortion metric if the bit rate decreases further in this paper we propose a new soft decoding approach to reduce the l2 distortion of l decoded images and retain the advantages of both minmax and least square approximations the soft decoding is performed in a framework of image restoration that exploits the tight error bounds afforded by the l constrained coding and employs a context modeler of quantization errors experimental results demonstrate that the l constrained hard decoded images can be restored to gain more than 2 db in peak signal to noise ratio psnr while still retaining tight error bounds on every single pixel the new soft decoding technique can even outperform jpeg 2000 a state of the art encoder optimized image codec for bit rates higher than 1 bpp a critical rate region for applications of near lossless image compression all the coding gains are made without increasing the encoder complexity as the heavy computations to gain coding efficiency are delegated to the decoder

nonlinear analysis of a differential difference equation with next nearest neighbour interaction for traffic flow we analyse the newell whitham type car following model described by a differential difference equation with a generalized optimal velocity function which depends not only on the headway of each car but also on the headway of the immediately preceding one linear stability analysis shows that the model is stabilized for larger delay time by taking into account the headway of the immediately preceding car from the nonlinear analysis the propagating kink solution for traffic jams is obtained the fundamental diagram and the relation between the headway and the delay time are examined by numerical simulation we find that the result from the nonlinear analysis is in good agreement with that obtained from the numerical simulation

effect of looking backward on traffic flow in an extended multiple car following model abstract in this paper a new car following model is proposed by incorporating the backward looking effect under certain conditions and multiple information of preceding cars in traffic flow and the neutral stability condition of this model can be obtained by using the linear stability theory numerical simulation shows that the proposed model is theoretically an improvement over previous ones