Results 1  10
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137
Local adaptivity to variable smoothness for exemplarbased image denoising and representation
, 2005
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Nonlinear inverse scale space methods for image restoration
 Communications in Mathematical Sciences
, 2005
"... Abstract. In this paper we generalize the iterated refinement method, introduced by the authors in [8], to a timecontinuous inverse scalespace formulation. The iterated refinement procedure yields a sequence of convex variational problems, evolving toward the noisy image. The inverse scale space m ..."
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Cited by 65 (18 self)
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Abstract. In this paper we generalize the iterated refinement method, introduced by the authors in [8], to a timecontinuous inverse scalespace formulation. The iterated refinement procedure yields a sequence of convex variational problems, evolving toward the noisy image. The inverse scale space method arises as a limit for a penalization parameter tending to zero, while the number of iteration steps tends to infinity. For the limiting flow, similar properties as for the iterated refinement procedure hold. Specifically, when a discrepancy principle is used as the stopping criterion, the error between the reconstruction and the noisefree image decreases until termination, even if only the noisy image is available and a bound on the variance of the noise is known. The inverse flow is computed directly for onedimensional signals, yielding high quality restorations. In higher spatial dimensions, we introduce a relaxation technique using two evolution equations. These equations allow accurate, efficient and straightforward implementation. 1
Unsupervised, informationtheoretic, adaptive image filtering for image restoration
 IEEE TRANS. PAMI
, 2006
"... Image restoration is an important and widely studied problem in computer vision and image processing. Various image filtering strategies have been effective, but invariably make strong assumptions about the properties of the signal and/or degradation. Hence, these methods lack the generality to be e ..."
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Cited by 53 (3 self)
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Image restoration is an important and widely studied problem in computer vision and image processing. Various image filtering strategies have been effective, but invariably make strong assumptions about the properties of the signal and/or degradation. Hence, these methods lack the generality to be easily applied to new applications or diverse image collections. This paper describes a novel unsupervised, informationtheoretic, adaptive filter (UINTA) that improves the predictability of pixel intensities from their neighborhoods by decreasing their joint entropy. In this way, UINTA automatically discovers the statistical properties of the signal and can thereby restore a wide spectrum of images. The paper describes the formulation to minimize the joint entropy measure and presents several important practical considerations in estimating neighborhood statistics. It presents a series of results on both real and synthetic data along with comparisons with current stateoftheart techniques, including novel applications to medical image processing.
Higherorder image statistics for unsupervised, informationtheoretic, adaptive, image filtering
 Proc. IEEE Int. Conf. Computer Vision Pattern Recog. 2005. S.P. Awate and R.T
"... The restoration of images is an important and widely studied problem in computer vision and image processing. Various image filtering strategies have been effective, but invariably make strong assumptions about the properties of the signal and/or degradation. Therefore, these methods typically lack ..."
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Cited by 29 (8 self)
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The restoration of images is an important and widely studied problem in computer vision and image processing. Various image filtering strategies have been effective, but invariably make strong assumptions about the properties of the signal and/or degradation. Therefore, these methods typically lack the generality to be easily applied to new applications or diverse image collections. This paper describes a novel unsupervised, informationtheoretic, adaptive filter (UINTA) that improves the predictability of pixel intensities from their neighborhoods by decreasing the joint entropy between them. Thus UINTA automatically discovers the statistical properties of the signal and can thereby restore a wide spectrum of images and applications. This paper describes the formulation required to minimize the joint entropy measure, presents several important practical considerations in estimating imageregion statistics, and then presents results on both real and synthetic data. 1.
Image deblurring in the presence of impulsive noise
 Int. J. Comput. Vision
, 2006
"... Consider the problem of image deblurring in the presence of impulsive noise. Standard image deconvolution methods rely on the Gaussian noise model and do not perform well with impulsive noise. The main challenge is to deblur the image, recover its discontinuities and at the same time remove the impu ..."
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Cited by 27 (2 self)
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Consider the problem of image deblurring in the presence of impulsive noise. Standard image deconvolution methods rely on the Gaussian noise model and do not perform well with impulsive noise. The main challenge is to deblur the image, recover its discontinuities and at the same time remove the impulse noise. Medianbased approaches are inadequate, because at high noise levels they induce nonlinear distortion that hampers the deblurring process. Distinguishing outliers from edge elements is difficult in current gradientbased edgepreserving restoration methods. The suggested approach integrates and extends the robust statistics, line process (half quadratic) and anisotropic diffusion points of view. We present a unified variational approach to image deblurring and impulse noise removal. The objective functional consists of a fidelity term and a regularizer. Data fidelity is quantified using the robust modified L 1 norm, and elements from the MumfordShah functional are used for regularization. We show that the MumfordShah regularizer can be viewed as an extended line process. It reflects spatial organization properties of the image edges, that do not appear in the common line process or anisotropic diffusion. This allows to distinguish outliers from edges and leads to superior experimental results. 1
R.: Unsupervised texture segmentation with nonparametric neighborhood statistics. Computer Vision–ECCV
, 2006
"... Abstract. This paper presents a novel approach to unsupervised texture segmentation that relies on a very general nonparametric statistical model of image neighborhoods. The method models image neighborhoods directly, without the construction of intermediate features. It does not rely on using spec ..."
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Cited by 24 (4 self)
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Abstract. This paper presents a novel approach to unsupervised texture segmentation that relies on a very general nonparametric statistical model of image neighborhoods. The method models image neighborhoods directly, without the construction of intermediate features. It does not rely on using specific descriptors that work for certain kinds of textures, but is rather based on a more generic approach that tries to adaptively capture the core properties of textures. It exploits the fundamental description of textures as images derived from stationary random fields and models the associated higherorder statistics nonparametrically. This general formulation enables the method to easily adapt to various kinds of textures. The method minimizes an entropybased metric on the probability density functions of image neighborhoods to give an optimal segmentation. The entropy minimization drives a very fast levelset scheme that uses threshold dynamics, which allows for a very rapid evolution towards the optimal segmentation during the initial iterations. The method does not rely on a training stage and, hence, is unsupervised. It automatically tunes its important internal parameters based on the information content of the data. The method generalizes in a straightforward manner from the tworegion case to an arbitrary number of regions and incorporates an efficient multiphase levelset framework. This paper presents numerous results, for both the twotexture and multipletexture cases, using synthetic and real images that include electronmicroscopy images. 1
Accurate Optical Flow in Noisy Image Sequences
, 2001
"... Optical Flow estimation in noisy image sequences requires a special denoising strategy. Towards this end we introduce a new tensordriven anisotropic diffusion scheme which is designed to enhance opticalflowlike spatiotemporal structures. This is achieved by selecting diffusivities in a special ma ..."
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Cited by 16 (4 self)
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Optical Flow estimation in noisy image sequences requires a special denoising strategy. Towards this end we introduce a new tensordriven anisotropic diffusion scheme which is designed to enhance opticalflowlike spatiotemporal structures. This is achieved by selecting diffusivities in a special manner depending on the eigenvalues of the well known structure tensor. We illustrate how the proposed choice differs from edge and coherenceenhancing anisotropic diffusion. Furthermore we extend a recently discovered discretization scheme for anisotropic diffusion to 3D data. An automatic stop criterion to terminate the diffusion after a suitable time is given. The performance of the introduced method is examined quantitatively using image sequences with a substantial amount of noise added. 1.
EXAMPLARBASED INPAINTING BASED ON LOCAL GEOMETRY
"... In this paper, we propose a novel inpainting algorithm combining the advantages of PDEbased schemes and examplarbased approaches. The proposed algorithm relies on the use of structure tensors to define the filling order priority and template matching. The structure tensors are computed in a hierar ..."
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Cited by 16 (3 self)
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In this paper, we propose a novel inpainting algorithm combining the advantages of PDEbased schemes and examplarbased approaches. The proposed algorithm relies on the use of structure tensors to define the filling order priority and template matching. The structure tensors are computed in a hierarchic manner whereas the template matching is based on a Knearest neighbor algorithm. The value K is adaptively set in function of the local texture information. Compared to two state of the art approaches, the proposed method provides more coherent results. Index Terms — inpainting, tensor, examplarbased. 1.
Deformable model with a complexity independent from image resolution
 Computer Vision and Image Understanding
"... We present a parametric deformable model which recovers image components with a complexity independent from the resolution of input images. The proposed model also automatically changes its topology and remains fully compatible with the general framework of deformable models. More precisely, the im ..."
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Cited by 15 (0 self)
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We present a parametric deformable model which recovers image components with a complexity independent from the resolution of input images. The proposed model also automatically changes its topology and remains fully compatible with the general framework of deformable models. More precisely, the image space is equipped with a metric that expands salient image details according to their strength and their curvature. During the whole evolution of the model, the sampling of the contour is kept regular with respect to this metric. By this way, the vertex density is reduced along most parts of the curve while a high quality of shape representation is preserved. The complexity of the deformable model is thus improved and is no longer influenced by featurepreserving changes in the resolution of input images. Building the metric requires a prior estimation of contour curvature. It is obtained using a robust estimator which investigates the local variations in the orientation of image gradient. Experimental results on both computer generated and biomedical images are presented to illustrate the advantages of our approach. Key words: deformable model, topology adaptation, resolution adaptation, curvature estimation, segmentation/reconstruction. 2 1
Gradient schemes: a generic framework for the discretisation of linear, nonlinear and nonlocal elliptic and parabolic equations
 Math. Models Methods Appl. Sci
"... Gradient schemes are nonconforming methods written in discrete variational formulation and based on independent approximations of functions and gradients, using the same degrees of freedom. Previous works showed that several wellknown methods fall in the framework of gradient schemes. Four properti ..."
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Cited by 14 (9 self)
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Gradient schemes are nonconforming methods written in discrete variational formulation and based on independent approximations of functions and gradients, using the same degrees of freedom. Previous works showed that several wellknown methods fall in the framework of gradient schemes. Four properties, namely coercivity, consistency, limitconformity and compactness, are shown in this paper to be sufficient to prove the convergence of gradient schemes for linear and nonlinear elliptic and parabolic problems, including the case of nonlocal operators arising for example in image processing. We also show that the Hybrid Mimetic Mixed family, which includes in particular the Mimetic Finite Difference schemes, may be seen as gradient schemes meeting these four properties, and therefore converges for the class of above mentioned problems. 1