image denoising in steerable pyramid domain based on a local laplace prior this paper presents a new image denoising algorithm based on the modeling of coefficients in each subband of steerable pyramid employing a laplacian probability density function pdf with local variance this pdf is able to model the heavy tailed nature of steerable pyramid coefficients and the empirically observed correlation between the coefficient amplitudes within this framework we describe a novel method for image denoising based on designing both maximum a posteriori map and minimum mean squared error mmse estimators which relies on the zero mean laplacian random variables with high local correlation despite the simplicity of our spatially adaptive denoising method both in its concern and implementation our denoising results achieves better performance than several published methods such as bayes least squared gaussian scale mixture bls gsm technique that is a state of the art denoising technique

ideal spatial adaptation by wavelet shrinkage summary with ideal spatial adaptation an oracle furnishes information about how best to adapt a spatially variable estimator whether piecewise constant piecewise polynomial variable knot spline or variable bandwidth kernel to the unknown function estimation with the aid of an oracle offers dramatic advantages over traditional linear estimation by nonadaptive kernels however it is a priori unclear whether such performance can be obtained by a procedure relying on the data alone we describe a new principle for spatially adaptive estimation selective wavelet reconstruction we show that variable knot spline fits and piecewise polynomial fits when equipped with an oracle to select the knots are not dramatically more powerful than selective wavelet reconstruction with an oracle we develop a practical spatially adaptive method riskshrink which works by shrinkage of empirical wavelet coefficients riskshrink mimics the performance of an oracle for selective wavelet reconstruction as well as it is possible to do so a new inequality in multivariate normal decision theory which we call the oracle inequality shows that attained performance differs from ideal performance by at most a factor of approximately 2 log n where n is the sample size moreover no estimator can give a better guarantee than this within the class of spatially adaptive procedures riskshrink is essentially optimal relying only on the data it comes within a factor log 2 n of the performance of piecewise polynomial and variableknot spline methods equipped with an oracle in contrast it is unknown how or if piecewise polynomial methods could be made to function this well when denied access to an oracle and forced to rely on data alone

an orientation selective orthogonal lapped transform a novel critically sampled orientation selective orthogonal lapped transform called the lapped hartley transform lht is derived in a first step overlapping basis functions are generated by modulating basis functions of a 2 d block hartley transform by a cosine wave to achieve invertibility and orthogonality an iterative filter is applied as prefilter in the analysis and as postfilter in the synthesis operation respectively alternatively filtering can be restricted to analysis or synthesis ending up with a biorthogonal transform lht pr lht po a statistical analysis based on a 4000 image data base shows that the lht and lht po have better redundancy removal properties than other block or lapped transforms finally image compression and noise removal examples are given showing the advantages of the lht especially in images containing oriented textures

estimation of the mean of a multivariate normal distribution estimation of the means of independent normal random variables is considered using sum of squared errors as loss an unbiased estimate of risk is obtained for an arbitrary estimate and certain special classes of estimates are then discussed the results are applied to smoothing by use of moving averages and to trimmed analogs of the james stein estimate a suggestion is made for calculating approximate confidence sets for the mean vector centered at an arbitrary estimate

a valued ferrers relation for interval comparison the paper deals with the valued comparison of intervals for decision making interval orders are classical preference structures where the comparison of intervals is done in an ordinal way in this paper we focus on valued comparison where more information especially the distance between end points of intervals is used in order to have more sophisticated preference structures the generalization of an interval order as a valued structure requires the choice of de morgan triplets we propose a valued outranking relation for interval comparison and show that it satisfies different definitions of valued interval orders using different de morgan triplet the decomposition of our outranking relation into preference and indifference provides a valued preference structure where the preference is t transitive and monotone

super resolution of multi observed rgb d images based on nonlocal regression and total variation there is growing demand for accuracy in image processing and visualization and the super resolution sr technique for multi observed rgb d images has become popular because it provides space redundant information and produces a detailed reconstruction even with a large magnification factor this technique has been thoroughly investigated in recent years nevertheless technical challenges remain such as finding sub pixel correspondences with low resolution lr observations exploiting space redundant information formulating space homogeneity constraints and leveraging cross image similarities in structures to address these challenges this paper proposes a unified optimization framework to estimate both the super resolved rgb image and the super resolved depth image from the multi observed lr rgb d images using their correlations using depth assisted cross image correspondences the rgb image sr problem is formulated as an effective regularization function by incorporating the normalized bilateral total variation regularizer and it is efficiently solved by a first order primal dual algorithm the depth image sr estimate can be obtained by minimizing a nonlocal regression based energy which integrates the structural cues of the super resolved rgb image in a detail preserving fashion essentially our unified optimization framework uses the rgb image and depth image as a priori knowledge that the sr process uses for better accuracy our extensive experiments on public rgb d benchmarks and real data and our quantitative comparison with several state of the art methods demonstrate the superiority of our method in terms of accuracy versatility and reliability of details and sharp feature preservation

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

local contrast phase descriptor for fingerprint liveness detection we propose a new local descriptor for fingerprint liveness detection the input image is analyzed both in the spatial and in the frequency domain in order to extract information on the local amplitude contrast and on the local behavior of the image synthesized by considering the phase of some selected transform coefficients these two pieces of information are used to generate a bi dimensional contrast phase histogram used as feature vector associated with the image after an appropriate feature selection a trained linear kernel svm classifier makes the final live fake decision experiments on the publicly available livdet 2011 database comprising datasets collected from various sensors prove the proposed method to outperform the state of the art liveness detection techniques highlightswe propose a new local descriptor for fingerprint liveness detection it is based on the joint use of contrast and phase information image analysis is carried out in both the spatial and the transform domains we generate a bi dimensional contrast phase histogram used as feature vector a properly trained linear kernel svm classifier makes the final live fake decision

speckle reduction via higher order total variation approach multiplicative noise also known as speckle reduction is a prerequisite for many image processing tasks in coherent imaging systems such as the synthetic aperture radar one approach extensively used in this area is based on total variation tv regularization which can recover significantly sharp edges of an image but suffers from the staircase like artifacts in order to overcome the undesirable deficiency we propose two novel models for removing multiplicative noise based on total generalized variation tgv penalty the tgv regularization has been mathematically proven to be able to eliminate the staircasing artifacts by being aware of higher order smoothness furthermore an efficient algorithm is developed for solving the tgv based optimization problems numerical experiments demonstrate that our proposed methods achieve state of the art results both visually and quantitatively in particular when the image has some higher order smoothness our methods outperform the tv based algorithms

llsure local linear sure based edge preserving image filtering in this paper we propose a novel approach for performing high quality edge preserving image filtering based on a local linear model and using the principle of stein s unbiased risk estimate as an estimator for the mean squared error from the noisy image only we derive a simple explicit image filter which can filter out noise while preserving edges and fine scale details moreover this filter has a fast and exact linear time algorithm whose computational complexity is independent of the filtering kernel size thus it can be applied to real time image processing tasks the experimental results demonstrate the effectiveness of the new filter for various computer vision applications including noise reduction detail smoothing and enhancement high dynamic range compression and flash no flash denoising

a new sure approach to image denoising interscale orthonormal wavelet thresholding this paper introduces a new approach to orthonormal wavelet image denoising instead of postulating a statistical model for the wavelet coefficients we directly parametrize the denoising process as a sum of elementary nonlinear processes with unknown weights we then minimize an estimate of the mean square error between the clean image and the denoised one the key point is that we have at our disposal a very accurate statistically unbiased mse estimate stein s unbiased risk estimate that depends on the noisy image alone not on the clean one like the mse this estimate is quadratic in the unknown weights and its minimization amounts to solving a linear system of equations the existence of this a priori estimate makes it unnecessary to devise a specific statistical model for the wavelet coefficients instead and contrary to the custom in the literature these coefficients are not considered random any more we describe an interscale orthonormal wavelet thresholding algorithm based on this new approach and show its near optimal performance both regarding quality and cpu requirement by comparing it with the results of three state of the art nonredundant denoising algorithms on a large set of test images an interesting fallout of this study is the development of a new group delay based parent child prediction in a wavelet dyadic tree

graph based methods for simultaneous smoothing and sharpening of color images abstract in this work we introduce an image characterization of pixels based on local graphs that allows to distinguish different local regions around a pixel this separation also permits us to develop a method for determining the role of each pixel in a neighborhood of any other either for smoothing or for sharpening two methods for simultaneously conducting both processes are provided our solution overcomes the drawbacks of the classic two steps sequential smoothing and sharpening process enhancing details while reducing noise and not losing critical information the parameters of the methods are adjusted in two different ways through observers visual quality optimization and with an objective optimization criterion the results show that our methods outperform other recent state of the art ones

fenchel duality based dictionary learning for restoration of noisy images dictionary learning based sparse modeling has been increasingly recognized as providing high performance in the restoration of noisy images although a number of dictionary learning algorithms have been developed most of them attack this learning problem in its primal form with little effort being devoted to exploring the advantage of solving this problem in a dual space in this paper a novel fenchel duality based dictionary learning fd dl algorithm has been proposed for the restoration of noise corrupted images with the restricted attention to the additive white gaussian noise the sparse image representation is formulated as an l2 l1 minimization problem whose dual formulation is constructed using a generalization of fenchel s duality theorem and solved under the augmented lagrangian framework the proposed algorithm has been compared with four state of the art algorithms including the local pixel grouping principal component analysis method of optimal directions k singular value decomposition and beta process factor analysis on grayscale natural images our results demonstrate that the fd dl algorithm can effectively improve the image quality and its noisy image restoration ability is comparable or even superior to the abilities of the other four widely used algorithms

predicting multivariate responses in multiple linear regression we look at the problem of predicting several response variables from the same set of explanatory variables the question is how to take advantage of correlations between the response variables to improve predictive accuracy compared with the usual procedure of doing individual regressions of each response variable on the common set of predictor variables a new procedure is introduced called the curds and whey method its use can substantially reduce prediction errors when there are correlations between responses while maintaining accuracy even if the responses are uncorrelated in extensive simulations the new procedure is compared with several previously proposed methods for predicting multiple responses including partial least squares and exhibits superior accuracy one version can be easily implemented in the context of standard statistical packages

image denoising via sparse and redundant representations over learned dictionaries we address the image denoising problem where zero mean white and homogeneous gaussian additive noise is to be removed from a given image the approach taken is based on sparse and redundant representations over trained dictionaries using the k svd algorithm we obtain a dictionary that describes the image content effectively two training options are considered using the corrupted image itself or training on a corpus of high quality image database since the k svd is limited in handling small image patches we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image we show how such bayesian treatment leads to a simple and effective denoising algorithm this leads to a state of the art denoising performance equivalent and sometimes surpassing recently published leading alternative denoising methods

blind quality estimator for 3d images based on binocular combination and extreme learning machine abstract over recent years blind quality estimators for three dimensional 3d images have received increasing attention in this paper we describe a blind quality estimator for 3d images based on binocular combination and an extreme learning machine elm for more accurate alignment with subjective human experience first two binocular combinations of stimuli are generated using different combination strategies various binocular quality aware features of these combinations are then extracted by local binary pattern operators which give an effective description of the degradation pattern finally these features are mapped to the subjective quality score of the distorted 3d image using an elm experimental results using three benchmark databases confirm that the proposed metric is effective and achieves competitive prediction performance when compared with most current full reference and blind metrics

the sure let approach to image denoising we propose a new approach to image denoising based on the image domain minimization of an estimate of the mean squared error stein s unbiased risk estimate sure unlike most existing denoising algorithms using the sure makes it needless to hypothesize a statistical model for the noiseless image a key point of our approach is that although the nonlinear processing is performed in a transformed domain typically an undecimated discrete wavelet transform but we also address nonorthonormal transforms this minimization is performed in the image domain indeed we demonstrate that when the transform is a ldquotightrdquo frame an undecimated wavelet transform using orthonormal filters separate subband minimization yields substantially worse results in order for our approach to be viable we add another principle that the denoising process can be expressed as a linear combination of elementary denoising processes linear expansion of thresholds let armed with the sure and let principles we show that a denoising algorithm merely amounts to solving a linear system of equations which is obviously fast and efficient quite remarkably the very competitive results obtained by performing a simple threshold image domain sure optimized on the undecimated haar wavelet coefficients show that the sure let principle has a huge potential

a nonlinear stein based estimator for multichannel image denoising the use of multicomponent images has become widespread with the improvement of multisensor systems having increased spatial and spectral resolutions however the observed images are often corrupted by an additive gaussian noise in this paper we are interested in multichannel image denoising based on a multiscale representation of the images a multivariate statistical approach is adopted to take into account both the spatial and the intercomponent correlations existing between the different wavelet subbands more precisely we propose a new parametric nonlinear estimator which generalizes many reported denoising methods the derivation of the optimal parameters is achieved by applying stein s principle in the multivariate case experiments performed on multispectral remote sensing images clearly indicate that our method outperforms conventional wavelet denoising techniques

regularization parameter selection for nonlinear iterative image restoration and mri reconstruction using gcv and sure based methods regularized iterative reconstruction algorithms for imaging inverse problems require selection of appropriate regularization parameter values we focus on the challenging problem of tuning regularization parameters for nonlinear algorithms for the case of additive possibly complex gaussian noise generalized cross validation gcv and weighted mean squared error mse approaches based on stein s unbiased risk estimate sure need the jacobian matrix of the nonlinear reconstruction operator representative of the iterative algorithm with respect to the data we derive the desired jacobian matrix for two types of nonlinear iterative algorithms a fast variant of the standard iterative reweighted least squares method and the contemporary split bregman algorithm both of which can accommodate a wide variety of analysis and synthesis type regularizers the proposed approach iteratively computes two weighted sure type measures predicted sure and projected sure which require knowledge of noise variance 2 and gcv which does not need 2 for these algorithms we apply the methods to image restoration and to magnetic resonance image mri reconstruction using total variation and an analysis type l1 regularization we demonstrate through simulations and experiments with real data that minimizing predicted sure and projected sure consistently lead to near mse optimal reconstructions we also observe that minimizing gcv yields reconstruction results that are near mse optimal for image restoration and slightly suboptimal for mri theoretical derivations in this paper related to jacobian matrix evaluations can be extended in principle to other types of regularizers and reconstruction algorithms

directional lapped orthogonal transform theory and design this paper proposes a directional design method of 2 d nonseparable linear phase paraunitary filter banks the proposed method is based on a lattice structure consisting of the 2 d separable dct block and nonseparable support extension processes because of the nonseparability the bases are allowed to be directional with the critically fixed subsampling overlapping orthogonal symmetric real valued and compact support properties first a novel vanishing moment vm condition is introduced as a suitable directional constraint where the moment is referred to as the trend vm the condition forces wavelet filters i e high pass and bandpass filters to annihilate trend surface components second some theoretical properties of tvms are discussed for general 2 d paraunitary systems and then the properties are applied to the lattice parameters in order to verify the significance several design examples are shown the trend surface annihilation properties are numerically confirmed and the denoising capability is evaluated for images through shrinkage it is shown that our proposed transforms yield perceptually preferable results

finite element techniques for removing the mixture of gaussian and impulsive noise the finite element method has become a very powerful and popular tool to solve boundary value problems coming from science and engineering here we consider a scattered data fitting method based on the finite element method and apply the method to remove the mixture of gaussian and impulsive noise from an image numerical results show the performance of the approach

blind image quality assessment based on high order statistics aggregation blind image quality assessment biqa research aims to develop a perceptual model to evaluate the quality of distorted images automatically and accurately without access to the non distorted reference images the state of the art general purpose biqa methods can be classified into two categories according to the types of features used the first includes handcrafted features which rely on the statistical regularities of natural images these however are not suitable for images containing text and artificial graphics the second includes learning based features which invariably require large codebook or supervised codebook updating procedures to obtain satisfactory performance these are time consuming and not applicable in practice in this paper we propose a novel general purpose biqa method based on high order statistics aggregation hosa requiring only a small codebook hosa consists of three steps first local normalized image patches are extracted as local features through a regular grid and a codebook containing 100 codewords is constructed by k means clustering in addition to the mean of each cluster the diagonal covariance and coskewness i e dimension wise variance and skewness of clusters are also calculated second each local feature is softly assigned to several nearest clusters and the differences of high order statistics mean variance and skewness between local features and corresponding clusters are softly aggregated to build the global quality aware image representation finally support vector regression is adopted to learn the mapping between perceptual features and subjective opinion scores the proposed method has been extensively evaluated on ten image databases with both simulated and realistic image distortions and shows highly competitive performance to the state of the art biqa methods

better subset regression using the nonnegative garrote a new method called the nonnegative nn garrote is proposed for doing subset regression it both shrinks and zeroes coefficients in tests on real and simulated data it produces lower prediction error than ordinary subset selection it is also compared to ridge regression if the regression equations generated by a procedure do not change drastically with small changes in the data the procedure is called stable subset selection is unstable ridge is very stable and the nn garrote is intermediate simulation results illustrate the effects of instability on prediction error

utilizing image scales towards totally training free blind image quality assessment a new approach to blind image quality assessment biqa requiring no training is proposed in this paper the approach is named as blind image quality evaluator based on scales and works by evaluating the global difference of the query image analyzed at different scales with the query image at original resolution the approach is based on the ability of the natural images to exhibit redundant information over various scales a distorted image is considered as a deviation from the natural image and bereft of the redundancy present in the original image the similarity of the original resolution image with its down scaled version will decrease more when the image is distorted more therefore the dissimilarities of an image with its low resolution versions are cumulated in the proposed method we dissolve the query image into its scale space and measure the global dissimilarity with the co occurrence histograms of the original and its scaled images these scaled images are the low pass versions of the original image the dissimilarity called low pass error is calculated by comparing the low pass versions across scales with the original image the high pass versions of the image in different scales are obtained by wavelet decomposition and their dissimilarity from the original image is also calculated this dissimilarity called high pass error is computed with the variance and gradient histograms and weighted by the contrast sensitivity function to make it perceptually effective these two kinds of dissimilarities are combined together to derive the quality score of the query image this method requires absolutely no training with the distorted image pristine images or subjective human scores to predict the perceptual quality but uses the intrinsic global change of the query image across scales the performance of the proposed method is evaluated across six publicly available databases and found to be competitive with the state of the art techniques

image denoising using scale mixtures of gaussians in the wavelet domain we describe a method for removing noise from digital images based on a statistical model of the coefficients of an overcomplete multiscale oriented basis neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables a gaussian vector and a hidden positive scalar multiplier the latter modulates the local variance of the coefficients in the neighborhood and is thus able to account for the empirically observed correlation between the coefficient amplitudes under this model the bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable we demonstrate through simulations with images contaminated by additive white gaussian noise that the performance of this method substantially surpasses that of previously published methods both visually and in terms of mean squared error

the curvelet transform for image denoising we describe approximate digital implementations of two new mathematical transforms namely the ridgelet transform and the curvelet transform our implementations offer exact reconstruction stability against perturbations ease of implementation and low computational complexity a central tool is fourier domain computation of an approximate digital radon transform we introduce a very simple interpolation in the fourier space which takes cartesian samples and yields samples on a rectopolar grid which is a pseudo polar sampling set based on a concentric squares geometry despite the crudeness of our interpolation the visual performance is surprisingly good our ridgelet transform applies to the radon transform a special overcomplete wavelet pyramid whose wavelets have compact support in the frequency domain our curvelet transform uses our ridgelet transform as a component step and implements curvelet subbands using a filter bank of a spl grave trous wavelet filters our philosophy throughout is that transforms should be overcomplete rather than critically sampled we apply these digital transforms to the denoising of some standard images embedded in white noise in the tests reported here simple thresholding of the curvelet coefficients is very competitive with state of the art techniques based on wavelets including thresholding of decimated or undecimated wavelet transforms and also including tree based bayesian posterior mean methods moreover the curvelet reconstructions exhibit higher perceptual quality than wavelet based reconstructions offering visually sharper images and in particular higher quality recovery of edges and of faint linear and curvilinear features existing theory for curvelet and ridgelet transforms suggests that these new approaches can outperform wavelet methods in certain image reconstruction problems the empirical results reported here are in encouraging agreement

patch based near optimal image denoising in this paper we propose a denoising method motivated by our previous analysis of the performance bounds for image denoising insights from that study are used here to derive a high performance practical denoising algorithm we propose a patch based wiener filter that exploits patch redundancy for image denoising our framework uses both geometrically and photometrically similar patches to estimate the different filter parameters we describe how these parameters can be accurately estimated directly from the input noisy image our denoising approach designed for near optimal performance in the mean squared error sense has a sound statistical foundation that is analyzed in detail the performance of our approach is experimentally verified on a variety of images and noise levels the results presented here demonstrate that our proposed method is on par or exceeding the current state of the art both visually and quantitatively

a no reference image sharpness metric based on structural information using sparse representation as a ubiquitous image distortion blur casts non trivial influence on image visual quality many image sharpness assessment methods have been proposed in the views of edge information gradient map frequency spectrum or other natural image statistics features in this paper we propose a no reference image sharpness metric based on structural information using sparse representation sr we observe that the dictionary atoms learned by sr algorithm convey clear structural information considering the distinct sensibility of human visual system hvs to different structures we use the learned dictionary to encode the patches of the blurry image to embed the locality of the representation a multi scale spatial max pooling scheme is incorporated the final sharpness score is given by an efficient linear support vector regression svr model we evaluate our approach on three public databases i e live ii tid2008 and csiq the experiments demonstrate that the proposed method achieves competitive performance compared with the state of the art blind image sharpness assessment algorithms

temporal coherence based deblurring using non uniform motion optimization non uniform motion blur due to object movement or camera jitter is a common phenomenon in videos however the state of the art video deblurring methods used to deal with this problem can introduce artifacts and may sometimes fail to handle motion blur due to the movements of the object or the camera in this paper we propose a non uniform motion model to deblur video frames the proposed method is based on superpixel matching in the video sequence to reconstruct sharp frames from blurry ones to identify a suitable sharp superpixel to replace a blurry one we enrich the search space with a non uniform motion blur kernel and use a generalized patchmatch algorithm to handle rotation scale and blur differences in the matching step instead of using pixel based or regular patch based representation we adopt a superpixel based representation and use color and motion to gather similar pixels our non uniform motion blur kernels are estimated from the motion field of these superpixels and our spatially varying motion model considers spatial and temporal coherence to find sharp superpixels experimental results showed that the proposed method can reconstruct sharp video frames from blurred frames caused by complex object and camera movements and performs better than the state of the art methods

weak and strong fuzzy interval orders abstract the purpose of this paper is to introduce and investigate the fuzzification of the classical interval order one of the most interesting classical preference structures without incomparability in this study we consider fuzzy preference structures as defined by de baets et al fuzzy preference structures without incomparability receive special attention their fuzzy preference and fuzzy large preference relations share certain types of the t n ferrers property two special types of the j n ferrers property are introduced the weak ferrers and strong ferrers properties the classical interval order is briefly reviewed t fuzzy interval orders are introduced and it is shown that their fuzzy preference relation is sup t transitive two special types of t fuzzy interval orders are considered weak and strong fuzzy interval orders corresponding to transforms of w and to m the particular intermediate role of the weak ferrers property of the fuzzy preference relation of a fps without incomparability is demonstrated on the one hand it is a necessary condition for the fps to be a strong fuzzy interval order while on the other hand it is a sufficient condition for this structure to be a weak fuzzy interval order finally the concept of an cut of a fps is introduced leading to an interesting characterization of the strong ferrers property of a fps without incomparability

generalized sure for exponential families applications to regularization stein s unbiased risk estimate sure was proposed by stein for the independent identically distributed i i d gaussian model in order to derive estimates that dominate least squares ls recently the sure criterion has been employed in a variety of denoising problems for choosing regularization parameters that minimize an estimate of the mean squared error mse however its use has been limited to the i i d case which precludes many important applications in this paper we begin by deriving a sure counterpart for general not necessarily i i d distributions from the exponential family this enables extending the sure design technique to a much broader class of problems based on this generalization we suggest a new method for choosing regularization parameters in penalized ls estimators we then demonstrate its superior performance over the conventional generalized cross validation and discrepancy approaches in the context of image deblurring and deconvolution the sure technique can also be used to design estimates without predefining their structure however allowing for too many free parameters impairs the estimate s performance to address this inherent tradeoff we propose a regularized sure objective and demonstrate its use in the context of wavelet denoising

image inpainting through metric labeling via guided patch mixing in this paper we present a novel formulation of exemplar based image inpainting as a metric labeling problem and solve it through the simulated annealing algorithm due to their greedy nature exemplar based methods sometimes produce inpainted images which are visually inconsistent these methods are highly dependent upon the initialization to solve these problems we generate five images with a different initialization a suitable mixture of these five images produces a good inpainted image the cost function of the proposed metric labeling problem consists of three components namely neighbor cost total variation cost and structure cost a linear combination among these components is used to maintain better visual consistency in the inpainted region having smooth transition from the bordering regions of the source image we use a quality measure to this end our experiments on a wide variety of images demonstrate that the proposed technique produces better inpainting images as compared with some other state of the art techniques

blind image quality assessment using joint statistics of gradient magnitude and laplacian features blind image quality assessment biqa aims to evaluate the perceptual quality of a distorted image without information regarding its reference image existing biqa models usually predict the image quality by analyzing the image statistics in some transformed domain e g in the discrete cosine transform domain or wavelet domain though great progress has been made in recent years biqa is still a very challenging task due to the lack of a reference image considering that image local contrast features convey important structural information that is closely related to image perceptual quality we propose a novel biqa model that utilizes the joint statistics of two types of commonly used local contrast features 1 the gradient magnitude gm map and 2 the laplacian of gaussian log response we employ an adaptive procedure to jointly normalize the gm and log features and show that the joint statistics of normalized gm and log features have desirable properties for the biqa task the proposed model is extensively evaluated on three large scale benchmark databases and shown to deliver highly competitive performance with state of the art biqa models as well as with some well known full reference image quality assessment models

viewing distance aware super resolution for high definition display in this paper we propose a novel algorithm for high definition displays to enlarge low resolution images while maintaining perceptual constancy i e the same field of view perceptual blur radius and the retinal image size in viewer s eyes we model the relationship between a viewer and a display by considering two main aspects of visual perception i e scaling factor and perceptual blur radius as long as we enlarge an image while adjust its image blur levels on the display we can maintain viewer s perceptual constancy we show that the scaling factor should be set in proportion to the viewing distance and the blur levels on the display should be adjusted according to the focal length of a viewer toward this we first refer to edge directions to interpolate a low resolution image with the increasing of viewing distance and the scaling factor after images are interpolated we utilize a local contrast to estimate the spatially varying image blur levels of the interpolated image we then further adjust the image blur levels using a parametric deblurring method which combines l1 as well as l2 reconstruction errors and tikhonov with total variation regularization terms by taking these factors into account high resolution images adaptive to viewing distance on a display can be generated experimental results on both natural image metric and user subjective studies across image scales demonstrate that the proposed super resolution algorithm for high definition displays performs favorably against the state of the art methods

no reference image quality assessment in the spatial domain we propose a natural scene statistic based distortion generic blind no reference nr image quality assessment iqa model that operates in the spatial domain the new model dubbed blind referenceless image spatial quality evaluator brisque does not compute distortion specific features such as ringing blur or blocking but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of naturalness in the image due to the presence of distortions thereby leading to a holistic measure of quality the underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model no transformation to another coordinate frame dct wavelet etc is required distinguishing it from prior nr iqa approaches despite its simplicity we are able to show that brisque is statistically better than the full reference peak signal to noise ratio and the structural similarity index and is highly competitive with respect to all present day distortion generic nr iqa algorithms brisque has very low computational complexity making it well suited for real time applications brisque features may be used for distortion identification as well to illustrate a new practical application of brisque we describe how a nonblind image denoising algorithm can be augmented with brisque in order to perform blind image denoising results show that brisque augmentation leads to performance improvements over state of the art methods a software release of brisque is available online http live ece utexas edu research quality brisque release zip for public use and evaluation

image denoising using svm classification in nonsubsampled contourlet transform domain for image denoising the main challenge is how to preserve the information bearing structures such as edges and textures to get satisfactory visual quality when improving the signal to noise ratio snr edge preserving image denoising has become a very intensive research topic in this paper we propose an image denoising using support vector machine svm classification in nonsubsampled contourlet transform nsct domain firstly the noisy image is decomposed into different subbands of frequency and orientation responses using the nsct secondly the feature vector for a pixel in a noisy image is formed by the spatial regularity in nsct domain and the least squares support vector machine ls m model is obtained by training then the nsct detail coefficients are divided into two classes edge related coefficients and noise related ones by ls svm training model finally the detail subbands of nsct coefficients are denoised by using shrink method in which the adaptive bayesian threshold is utilized extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state of the art denoising techniques especially the proposed method can preserve edges very well while removing noise

a sure approach for digital signal image deconvolution problems in this paper we are interested in the classical problem of restoring data degraded by a convolution and the addition of a white gaussian noise the originality of the proposed approach is twofold first we formulate the restoration problem as a nonlinear estimation problem leading to the minimization of a criterion derived from stein s unbiased quadratic risk estimate secondly the deconvolution procedure is performed using any analysis and synthesis frames that can be overcomplete or not new theoretical results concerning the calculation of the variance of the stein s risk estimate are also provided in this work simulations carried out on natural images show the good performance of our method with respect to conventional wavelet based restoration methods

a no reference objective image sharpness metric based on the notion of just noticeable blur jnb this work presents a perceptual based no reference objective image sharpness blurriness metric by integrating the concept of just noticeable blur into a probability summation model unlike existing objective no reference image sharpness blurriness metrics the proposed metric is able to predict the relative amount of blurriness in images with different content results are provided to illustrate the performance of the proposed perceptual based sharpness metric these results show that the proposed sharpness metric correlates well with the perceived sharpness being able to predict with high accuracy the relative amount of blurriness in images with different content

bivariate shrinkage functions for wavelet based denoising exploiting interscale dependency most simple nonlinear thresholding rules for wavelet based denoising assume that the wavelet coefficients are independent however wavelet coefficients of natural images have significant dependencies we only consider the dependencies between the coefficients and their parents in detail for this purpose new non gaussian bivariate distributions are proposed and corresponding nonlinear threshold functions shrinkage functions are derived from the models using bayesian estimation theory the new shrinkage functions do not assume the independence of wavelet coefficients we show three image denoising examples in order to show the performance of these new bivariate shrinkage rules in the second example a simple subband dependent data driven image denoising system is described and compared with effective data driven techniques in the literature namely visushrink sureshrink bayesshrink and hidden markov models in the third example the same idea is applied to the dual tree complex wavelet coefficients

automatic parameter selection for denoising algorithms using a no reference measure of image content across the field of inverse problems in image and video processing nearly all algorithms have various parameters which need to be set in order to yield good results in practice usually the choice of such parameters is made empirically with trial and error if no ground truth reference is available some analytical methods such as cross validation and stein s unbiased risk estimate sure have been successfully used to set such parameters however these methods tend to be strongly reliant on restrictive assumptions on the noise and also computationally heavy in this paper we propose a no reference metric q which is based upon singular value decomposition of local image gradient matrix and provides a quantitative measure of true image content i e sharpness and contrast as manifested in visually salient geometric features such as edges in the presence of noise and other disturbances this measure 1 is easy to compute 2 reacts reasonably to both blur and random noise and 3 works well even when the noise is not gaussian the proposed measure is used to automatically and effectively set the parameters of two leading image denoising algorithms ample simulated and real data experiments support our claims furthermore tests using the tid2008 database show that this measure correlates well with subjective quality evaluations for both blur and noise distortions

local gradient patterns lgp an effective local statistical feature extraction scheme for no reference image quality assessment abstract gradient features are known to be effective for full reference fr image quality assessment iqa however only a few metrics utilize gradient features for no reference nr iqa to investigate the potential benefits of gradient magnitude and phase for nr iqa we propose a novel and effective local statistical feature extraction metric namely local gradient patterns lgp for general purpose nr iqa using a gaussian partial derivative filter the image is first decomposed into two complementary components gradient magnitude and phase the local statistical features e g the conditional probability distributions are then extracted from the complementary components using the derived local gradient magnitude and phase pattern operators finally to facilitate nr iqa local statistical features that convey important structural information are mapped to the subjective mean opinion score of the image using a support vector regression svr procedure we evaluated our proposed lgp metric using images from two publicly available test databases the results confirm that the proposed lgp metric provides predictive performance that is superior to most state of the art nr iqa metrics and has an acceptable level of computational complexity

an efficient denoising framework using weighted overlapping group sparsity abstract in group sparse signal applications components within each group experience different scales of magnitude this is due to the fact that the components of each group exhibit a large but not isolated range of values in this paper weights are introduced to balance the different scales of the components within each group these added weights improve the accuracy and the stability of the reconstruction the proposed method weighted overlapping group sparsity wolgs uses the weighted l2 norm within each group and the l1 norm across the groups it is observed that the introduction of weights in convex and non convex penalty functions produces smaller root mean square errors rmse of the reconstructed signal when compared to the case of not using weights the method is extended to image denoising using only weighted convex penalty function and an improved quality of the reconstructed image is evaluated using brisque ssim and psnr furthermore the proposed algorithm produces a rmse smaller than that of the two state of the art methods which do not use overlapping group sparsity olgs

toward a no reference image quality assessment using statistics of perceptual color descriptors analysis of the statistical properties of natural images has played a vital role in the design of no reference nr image quality assessment iqa techniques in this paper we propose parametric models describing the general characteristics of chromatic data in natural images they provide informative cues for quantifying visual discomfort caused by the presence of chromatic image distortions the established models capture the correlation of chromatic data between spatially adjacent pixels by means of color invariance descriptors the use of color invariance descriptors is inspired by their relevance to visual perception since they provide less sensitive descriptions of image scenes against viewing geometry and illumination variations than luminances in order to approximate the visual quality perception of chromatic distortions we devise four parametric models derived from invariance descriptors representing independent aspects of color perception 1 hue 2 saturation 3 opponent angle and 4 spherical angle the practical utility of the proposed models is examined by deploying them in our new general purpose nr iqa metric the metric initially estimates the parameters of the proposed chromatic models from an input image to constitute a collection of quality aware features qaf thereafter a machine learning technique is applied to predict visual quality given a set of extracted qafs experimentation performed on large scale image databases demonstrates that the proposed metric correlates well with the provided subjective ratings of image quality over commonly encountered achromatic and chromatic distortions indicating that it can be deployed on a wide variety of color image processing problems as a generalized iqa solution

hybrid no reference natural image quality assessment of noisy blurry jpeg2000 and jpeg images in this paper we propose a new image quality assessment method based on a hybrid of curvelet wavelet and cosine transforms called hybrid no reference hnr model from the properties of natural scene statistics the peak coordinates of the transformed coefficient histogram of filtered natural images occupy well defined clusters in peak coordinate space which makes nr possible compared to other methods hnr has three benefits 1 it is an nr method applicable to arbitrary images without compromising the prediction accuracy of full reference methods 2 as far as we know it is the only general nr method well suited for four types of filters noise blur jpeg2000 and jpeg compression and 3 it can classify the filter types of the image and predict filter levels even when the image is results from the application of two different filters we tested hnr on very intensive video image database our image library and laboratory for image video engineering a public library results are compared to the state of the art methods including peak snr structural similarity visual information fidelity and so on

new support vector algorithms we propose a new class of support vector algorithms for regression and classification in these algorithms a parameter lets one effectively control the number of support vectors while this can be useful in its own right the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm the accuracy parameter epsilon in the regression case and the regularization constant c in the classification case we describe the algorithms give some theoretical results concerning the meaning and the choice of and report experimental results

spatially adaptive wavelet thresholding with context modeling for image denoising the method of wavelet thresholding for removing noise or denoising has been researched extensively due to its effectiveness and simplicity much of the literature has focused on developing the best uniform threshold or best basis selection however not much has been done to make the threshold values adaptive to the spatially changing statistics of images such adaptivity can improve the wavelet thresholding performance because it allows additional local information of the image such as the identification of smooth or edge regions to be incorporated into the algorithm this work proposes a spatially adaptive wavelet thresholding method based on context modeling a common technique used in image compression to adapt the coder to changing image characteristics each wavelet coefficient is modeled as a random variable of a generalized gaussian distribution with an unknown parameter context modeling is used to estimate the parameter for each coefficient which is then used to adapt the thresholding strategy this spatially adaptive thresholding is extended to the overcomplete wavelet expansion which yields better results than the orthogonal transform experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower mse than the best uniform thresholding with the original image assumed known

novel speed up strategies for non local means denoising with patch and edge patch based dictionaries in this paper a novel technique to speed up a non local means nlm filter is proposed in the original nlm filter most of its computational time is spent on finding distances for all the patches in the search window here we build a dictionary in which patches with similar photometric structures are clustered together dictionary is built only once with high resolution images belonging to different scenes since the dictionary is well organized in terms of indexing its entries it is used to search similar patches very quickly for efficient nlm denoising we achieve a substantial reduction in computational cost compared with the original nlm method especially when the search window of nlm is large without much affecting the psnr second we show that by building a dictionary for edge patches as opposed to intensity patches it is possible to reduce the dictionary size thus further improving the computational speed and memory requirement the proposed method preclassifies similar patches with the same distance measure as used by nlm method the proposed algorithm is shown to outperform other prefiltering based fast nlm algorithms computationally as well as qualitatively

image denoising via bandwise adaptive modeling and regularization exploiting nonlocal similarity this paper proposes a new image denoising algorithm based on adaptive signal modeling and regularization it improves the quality of images by regularizing each image patch using bandwise distribution modeling in transform domain instead of using a global model for all the patches in an image it employs content dependent adaptive models to address the non stationarity of image signals and also the diversity among different transform bands the distribution model is adaptively estimated for each patch individually it varies from one patch location to another and also varies for different bands in particular we consider the estimated distribution to have non zero expectation to estimate the expectation and variance parameters for every band of a particular patch we exploit the nonlocal correlation in image to collect a set of highly similar patches as the data samples to form the distribution irrelevant patches are excluded so that such adaptively learned model is more accurate than a global one the image is ultimately restored via bandwise adaptive soft thresholding based on a laplacian approximation of the distribution of similar patch group transform coefficients experimental results demonstrate that the proposed scheme outperforms several state of the art denoising methods in both the objective and the perceptual qualities

the estimation of laplace random vectors in additive white gaussian noise this paper develops and compares the maximum a posteriori map and minimum mean square error mmse estimators for spherically contoured multivariate laplace random vectors in additive white gaussian noise the mmse estimator is expressed in closed form using the generalized incomplete gamma function we also find a computationally efficient yet accurate approximation for the mmse estimator in addition this paper develops an expression for the mse for any estimator of spherically contoured multivariate laplace random vectors in additive white gaussian noise awgn the development of which again depends on the generalized incomplete gamma function the estimators are motivated and tested on the problem of wavelet based image denoising

estimating the probability of the presence of a signal of interest in multiresolution single and multiband image denoising we develop three novel wavelet domain denoising methods for subband adaptive spatially adaptive and multivalued image denoising the core of our approach is the estimation of the probability that a given coefficient contains a significant noise free component which we call signal of interest in this respect we analyze cases where the probability of signal presence is 1 fixed per subband 2 conditioned on a local spatial context and 3 conditioned on information from multiple image bands all the probabilities are estimated assuming a generalized laplacian prior for noise free subband data and additive white gaussian noise the results demonstrate that the new subband adaptive shrinkage function outperforms bayesian thresholding approaches in terms of mean squared error the spatially adaptive version of the proposed method yields better results than the existing spatially adaptive ones of similar and higher complexity the performance on color and on multispectral images is superior with respect to recent multiband wavelet thresholding

a plea for the use of lukasiewicz triplets in the definition of fuzzy preference structures ii the identity case abstract it has been shown in the first part of this paper that the concept of a fuzzy preference structure is only meaningful provided that the de morgan triplet involved contains a continuous archimedean triangular norm having zero divisors or hence a transform of the ukasiewicz triangular norm in this second part additional arguments for this statement are supplied in what we call the identity case is the identity mapping and the involutive negator is the standard negator first it is shown that the use of a continuous non archimedean triangular norm having zero divisors in the definition of a fuzzy preference structure indeed is possible secondly using such a triangular norm implies that at least in the square 0 4 5 2 it actually behaves as the ukasiewicz triangular norm furthermore an important transformation theorem indicates that any fuzzy preference structure with respect to a de morgan triplet containing such a continuous non archimedean triangular norm having zero divisors can be transformed into a fuzzy preference structure with respect to the standard ukasiewicz triplet these additional arguments conclude in a convincing way our plea for the use of ukasiewicz triplets in the definition of fuzzy preference structures

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

multiple level feature based measure for retargeted image quality objective image retargeting quality assessment aims to use computational models to predict the retargeted image quality consistent with subjective perception in this paper we propose a multiple level feature mlf based quality measure to predict the perceptual quality of retargeted images we first provide an in depth analysis on the low level aspect ratio similarity feature and then propose a mid level edge group similarity feature to better address the shape structure related distortion furthermore a high level face block similarity feature is designed to deal with sensitive region deformation the multiple level features are complementary as they quantify different aspects of quality degradation in the retargeted image and the mlf measure learned by regression is used to predict the perceptual quality of retargeted images extensive experimental results performed on two public benchmark databases demonstrate that the proposed mlf measure achieves higher quality prediction accuracy than the existing relevant state of the art quality measures

pure let image deconvolution we propose a non iterative image deconvolution algorithm for data corrupted by poisson or mixed poisson gaussian noise many applications involve such a problem ranging from astronomical to biological imaging we parameterize the deconvolution process as a linear combination of elementary functions termed as linear expansion of thresholds this parameterization is then optimized by minimizing a robust estimate of the true mean squared error the poisson unbiased risk estimate each elementary function consists of a wiener filtering followed by a pointwise thresholding of undecimated haar wavelet coefficients in contrast to existing approaches the proposed algorithm merely amounts to solving a linear system of equations which has a fast and exact solution simulation experiments over different types of convolution kernels and various noise levels indicate that the proposed method outperforms the state of the art techniques in terms of both restoration quality and computational complexity finally we present some results on real confocal fluorescence microscopy images and demonstrate the potential applicability of the proposed method for improving the quality of these images

on the selection of optimum savitzky golay filters savitzky golay s g filters are finite impulse response lowpass filters obtained while smoothing data using a local least squares ls polynomial approximation savitzky and golay proved in their hallmark paper that local ls fitting of polynomials and their evaluation at the mid point of the approximation interval is equivalent to filtering with a fixed impulse response the problem that we address here is how to choose a pointwise minimum mean squared error mmse s g filter length or order for smoothing while preserving the temporal structure of a time varying signal we solve the bias variance tradeoff involved in the mmse optimization using stein s unbiased risk estimator sure we observe that the 3 db cutoff frequency of the sure optimal s g filter is higher where the signal varies fast locally and vice versa essentially enabling us to suitably trade off the bias and variance thereby resulting in near mmse performance at low signal to noise ratios snrs it is seen that the adaptive filter length algorithm performance improves by incorporating a regularization term in the sure objective function we consider the algorithm performance on real world electrocardiogram ecg signals the results exhibit considerable snr improvement noise performance analysis shows that the proposed algorithms are comparable and in some cases better than some standard denoising techniques available in the literature

monte carlo sure a black box optimization of regularization parameters for general denoising algorithms we consider the problem of optimizing the parameters of a given denoising algorithm for restoration of a signal corrupted by white gaussian noise to achieve this we propose to minimize stein s unbiased risk estimate sure which provides a means of assessing the true mean squared error mse purely from the measured data without need for any knowledge about the noise free signal specifically we present a novel monte carlo technique which enables the user to calculate sure for an arbitrary denoising algorithm characterized by some specific parameter setting our method is a black box approach which solely uses the response of the denoising operator to additional input noise and does not ask for any information about its functional form this therefore permits the use of sure for optimization of a wide variety of denoising algorithms we justify our claims by presenting experimental results for sure based optimization of a series of popular image denoising algorithms such as total variation denoising wavelet soft thresholding and wiener filtering smoothing splines in the process we also compare the performance of these methods we demonstrate numerically that sure computed using the new approach accurately predicts the true mse for all the considered algorithms we also show that sure uncovers the optimal values of the parameters in all cases

image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain this correspondence proposes an efficient algorithm for removing gaussian noise from corrupted image by incorporating a wavelet based trivariate shrinkage filter with a spatial based joint bilateral filter in the wavelet domain the wavelet coefficients are modeled as trivariate gaussian distribution taking into account the statistical dependencies among intrascale wavelet coefficients and then a trivariate shrinkage filter is derived by using the maximum a posteriori map estimator although wavelet based methods are efficient in image denoising they are prone to producing salient artifacts such as low frequency noise and edge ringing which relate to the structure of the underlying wavelet on the other hand most spatial based algorithms output much higher quality denoising image with less artifacts however they are usually too computationally demanding in order to reduce the computational cost we develop an efficient joint bilateral filter by using the wavelet denoising result rather than directly processing the noisy image in the spatial domain this filter could suppress the noise while preserve image details with small computational cost extension to color image denoising is also presented we compare our denoising algorithm with other denoising techniques in terms of psnr and visual quality the experimental results indicate that our algorithm is competitive with other denoising techniques

adaptive wavelet thresholding for image denoising and compression the first part of this paper proposes an adaptive data driven threshold for image denoising via wavelet soft thresholding the threshold is derived in a bayesian framework and the prior used on the wavelet coefficients is the generalized gaussian distribution ggd widely used in image processing applications the proposed threshold is simple and closed form and it is adaptive to each subband because it depends on data driven estimates of the parameters experimental results show that the proposed method called bayesshrink is typically within 5 of the mse of the best soft thresholding benchmark with the image assumed known it also outperforms sureshrink donoho and johnstone 1994 1995 donoho 1995 most of the time the second part of the paper attempts to further validate claims that lossy compression can be used for denoising the bayesshrink threshold can aid in the parameter selection of a coder designed with the intention of denoising and thus achieving simultaneous denoising and compression specifically the zero zone in the quantization step of compression is analogous to the threshold value in the thresholding function the remaining coder design parameters are chosen based on a criterion derived from rissanen s minimum description length mdl principle experiments show that this compression method does indeed remove noise significantly especially for large noise power however it introduces quantization noise and should be used only if bitrate were an additional concern to denoising

image denoising by sparse 3 d transform domain collaborative filtering we propose a novel image denoising strategy based on an enhanced sparse representation in transform domain the enhancement of the sparsity is achieved by grouping similar 2d image fragments e g blocks into 3d data arrays which we call groups collaborative altering is a special procedure developed to deal with these 3d groups we realize it using the three successive steps 3d transformation of a group shrinkage of the transform spectrum and inverse 3d transformation the result is a 3d estimate that consists of the jointly filtered grouped image blocks by attenuating the noise the collaborative filtering reveals even the finest details shared by grouped blocks and at the same time it preserves the essential unique features of each individual block the filtered blocks are then returned to their original positions because these blocks are overlapping for each pixel we obtain many different estimates which need to be combined aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy a significant improvement is obtained by a specially developed collaborative wiener filtering an algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail an extension to color image denoising is also developed the experimental results demonstrate that this computationally scalable algorithm achieves state of the art denoising performance in terms of both peak signal to noise ratio and subjective visual quality

oriented correlation models of distorted natural images with application to natural stereopair quality evaluation in recent years bandpass statistical models of natural photographic images of the world have been used with great success to solve highly diverse problems involving image representation image repair image quality assessment iqa and image compression one missing element has been a reliable and generic model of spatial image correlation that reflects the distributions of oriented and relatively oriented spatial structures we have developed such a model for bandpass pristine images and have generalized it here to also capture the spatial correlation structure of bandpass distorted images the model applies well to both luminance and depth images as a demonstration of the usefulness of the generalized model we develop a new no reference stereoscopic 3d iqa framework dubbed stereoscopic 3d blind image naturalness quality index which utilizes both univariate and generalized bivariate natural scene statistics nss models we first validate the robustness and effectiveness of these novel bivariate and correlation nss features extracted from distorted stereopairs then demonstrate that they are predictive of distortion severity our experimental results show that the resulting 3d image quality predictor based in part on the new model outperforms state of the art full and no reference 3d iqa algorithms on both symmetrically and asymmetrically distorted stereoscopic image pairs

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

shearlet based deconvolution in this paper a new type of deconvolution algorithm is proposed that is based on estimating the image from a shearlet decomposition shearlets provide a multidirectional and multiscale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets constructions such as curvelets and contourlets share similar properties yet their implementations are significantly different from that of shearlets taking advantage of unique properties of a new m channel implementation of the shearlet transform we develop an algorithm that allows for the approximation inversion operator to be controlled on a multiscale and multidirectional basis a key improvement over closely related approaches such as forward is the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation gcv various tests show that this method can perform significantly better than many competitive deconvolution algorithms

a plea for the use of lukasiewicz triplets in the definition of fuzzy preference structures i general argumentation abstract the generalization of the concept of a classical or crisp preference structure to that of a fuzzy preference structure expressing degrees of strict preference indifference and incomparability among a set of alternatives requires the choice of a de morgan triplet i e of a triangular norm and an involutive negator the resulting concept is only meaningful provided that this choice allows the representation of truly fuzzy preferences more specifically one of the degrees of strict preference indifference or incomparability should always be unconstrained to the preference modeller this intuitive requirement is violated when choosing a triangular norm without zero divisors since in that case fuzzy preference structures reduce to classical preference structures and hence none of the degrees can be freely assigned furthermore it is shown that the choice of a continuous non archimedean triangular norm having zero divisors is not compatible with our basic requirement the sets of degrees of strict preference indifference and incomparability in 0 1 are always bounded from above by a value strictly smaller than 1 these fundamental results imply that when working with a continuous triangular norm only archimedean ones having zero divisors are suitable candidates these arguments sufficiently support our plea for the use of ukasiewicz triplets in the definition of fuzzy preference structures

bayesian estimation for continuous time sparse stochastic processes we consider continuous time sparse stochastic processes from which we have only a finite number of noisy noiseless samples our goal is to estimate the noiseless samples denoising and the signal in between interpolation problem by relying on tools from the theory of splines we derive the joint a priori distribution of the samples and show how this probability density function can be factorized the factorization enables us to tractably implement the maximum a posteriori and minimum mean square error mmse criteria as two statistical approaches for estimating the unknowns we compare the derived statistical methods with well known techniques for the recovery of sparse signals such as the l1 norm and log l1 l0 relaxation regularization methods the simulation results show that under certain conditions the performance of the regularization techniques can be very close to that of the mmse estimator

building robust wavelet estimators for multicomponent images using stein s principle multichannel imaging systems provide several observations of the same scene which are often corrupted by noise in this paper we are interested in multispectral image denoising in the wavelet domain we adopt a multivariate statistical approach in order to exploit the correlations existing between the different spectral components our main contribution is the application of stein s principle to build a new estimator for arbitrary multichannel images embedded in additive gaussian noise simulation tests carried out on optical satellite images show that the proposed method outperforms conventional wavelet shrinkage techniques

image sharpness assessment based on local phase coherence sharpness is an important determinant in visual assessment of image quality the human visual system is able to effortlessly detect blur and evaluate sharpness of visual images but the underlying mechanism is not fully understood existing blur sharpness evaluation algorithms are mostly based on edge width local gradient or energy reduction of global local high frequency content here we understand the subject from a different perspective where sharpness is identified as strong local phase coherence lpc near distinctive image features evaluated in the complex wavelet transform domain previous lpc computation is restricted to be applied to complex coefficients spread in three consecutive dyadic scales in the scale space here we propose a flexible framework that allows for lpc computation in arbitrary fractional scales we then develop a new sharpness assessment algorithm without referencing the original image we use four subject rated publicly available image databases to test the proposed algorithm which demonstrates competitive performance when compared with state of the art algorithms

a detail based method for linear full reference image quality prediction in this paper a novel full reference method is proposed for image quality assessment using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details to this purpose the gradient of the impaired image is locally decomposed as a predicted version of the original gradient plus a gradient residual it is assumed that the detail attenuation identifies the detail loss whereas the gradient residuals describe the spurious details it turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional fisher information while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio the affine combination of these two metrics forms a new index strongly correlated with the empirical differential mean opinion score dmos for a significant class of image impairments as verified for three independent popular databases the method allowed alignment and merging of dmos data coming from these different databases to a common dmos scale by affine transformations unexpectedly the dmos scale setting is possible by the analysis of a single image affected by additive noise

optimal denoising in redundant representations image denoising methods are often designed to minimize mean squared error mse within the subbands of a multiscale decomposition however most high quality denoising results have been obtained with overcomplete representations for which minimization of mse in the subband domain does not guarantee optimal mse performance in the image domain we prove that despite this suboptimality the expected image domain mse resulting from applying estimators to subbands that are made redundant through spatial replication of basis functions e g cycle spinning is always less than or equal to that resulting from applying the same estimators to the original nonredundant representation in addition we show that it is possible to further exploit overcompleteness by jointly optimizing the subband estimators for image domain mse we develop an extended version of stein s unbiased risk estimate sure that allows us to perform this optimization adaptively for each observed noisy image we demonstrate this methodology using a new class of estimator formed from linear combinations of localized ldquobumprdquo functions that are applied either pointwise or on local neighborhoods of subband coefficients we show through simulations that the performance of these estimators applied to overcomplete subbands and optimized for image domain mse is substantially better than that obtained when they are optimized within each subband this performance is in turn substantially better than that obtained when they are optimized for use on a nonredundant representation

an alternative definition for fuzzy interval orders the class of interval orders is one of the most studied classes of preference structures without incomparability in the theory of classical preference modelling in this paper we propose a generalization of the classical definition of interval order and strict interval order we focus our attention on two important classes of interval orders and strict interval orders which are respectively defined by means of a strong de morgan triplet and a min max de morgan triplet

removal of correlated noise by modeling the signal of interest in the wavelet domain images captured with digital imaging devices often contain noise in literature many algorithms exist for the removal of white uncorrelated noise but they usually fail when applied to images with correlated noise in this paper we design a new denoising method for the removal of correlated noise by modeling the significance of the noise free wavelet coefficients in a local window using a new significance measure that defines the ldquosignal of interestrdquo and that is applicable to correlated noise we combine the intrascale model with a hidden markov tree model to capture the interscale dependencies between the wavelet coefficients we propose a denoising method based on the combined model and a less redundant wavelet transform we present results that show that the new method performs as well as the state of the art wavelet based methods while having a lower computational complexity

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

estimation of a loss function for spherically symmetric distributions in the general linear model this paper is concerned with estimating the loss of a point estimator when sampling from a spherically symmetric distribution we examine the canonical setting of a general linear model where the dimension of the parameter space is greater than 4 and less than the dimension of the sampling space we consider two location estimators the least squares estimator and a shrinkage estimator and we compare their unbiased loss estimator with an improved loss estimator the domination results are valid for a large class of spherically symmetric distributions and in so far as the sampling distribution does not need to be precisely specified the estimates have desirable robustness properties