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Numerical methods for pharmonic flows and applications to image processing (2003)

by L A Vese, S J Osher
Venue:SIAM J. of Num. Anal
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An iterative regularization method for total variation-based image restoration

by Stanley Osher, Martin Burger, Donald Goldfarb, Jinjun Xu, Wotao Yin - MULTISCALE MODEL. SIMUL. , 2005
"... We introduce a new iterative regularization procedure for inverse problems based on the use of Bregman distances, with particular focus on problems arising in image processing. We are motivated by the problem of restoring noisy and blurry images via variational methods by using total variation regu ..."
Abstract - Cited by 195 (29 self) - Add to MetaCart
We introduce a new iterative regularization procedure for inverse problems based on the use of Bregman distances, with particular focus on problems arising in image processing. We are motivated by the problem of restoring noisy and blurry images via variational methods by using total variation regularization. We obtain rigorous convergence results and effective stopping criteria for the general procedure. The numerical results for denoising appear to give significant improvement over standard models, and preliminary results for deblurring/denoising are very encouraging.
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... u2 = arg min u∈BV (Ω) (f − u) 2 � . This minimization procedure attempts to match normals as well as grey-level values. In [29] the denoised normal �n1 was computed by using a one-harmonic map as in =-=[46]-=-: �� �n1 = arg min |�n|=1 � � |∇�n| + λ �n − ∇f � � 2 . |∇f| Unlike all the other methods discussed in this paper, this is not a convex minimization problem, and it does not produce an image u1 satisf...

O.: Regularizing flows for constrained matrix-valued images

by D. Tschumperlé, R. Deriche, O. Faugeras - J. Math. Imaging Vision , 2004
"... Abstract. Nonlinear diffusion equations are now widely used to restore and enhance images. They allow to eliminate noise and artifacts while preserving large global features, such as object contours. In this context, we propose a differential-geometric framework to define PDEs acting on some manifol ..."
Abstract - Cited by 47 (8 self) - Add to MetaCart
Abstract. Nonlinear diffusion equations are now widely used to restore and enhance images. They allow to eliminate noise and artifacts while preserving large global features, such as object contours. In this context, we propose a differential-geometric framework to define PDEs acting on some manifold constrained datasets. We consider the case of images taking value into matrix manifolds defined by orthogonal and spectral constraints. We directly incorporate the geometry and natural metric of the underlying configuration space (viewed as a Lie group or a homogeneous space) in the design of the corresponding flows. Our numerical implementation relies on structure-preserving integrators that respect intrinsically the constraints geometry. The efficiency and versatility of this approach are illustrated through the anisotropic smoothing of diffusion tensor volumes in medical imaging. Note: This is the draft
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...c formulation of the Total Variation (TV) restoration model was given in [8], Beltrami flows were proposed in [23, 34, 4], and nonlinear heat equations borrowed from harmonic theory were presented in =-=[25, 35, 43]-=-. Most of these approaches were applied in the case where the target manifold was the unit hypersphere Sn−1 . The problem of building PDEs acting on fields of orthogonal matrices was also discussed in...

Orthonormal Vector Sets Regularization with PDE’s and Applications

by David Tschumperlé, Rachid Deriche , 2002
"... We are interested in regularizing fields of orthonormal vector sets, using constraint-preserving anisotropic diffusion PDE’s. Each point of such a field is defined by multiple orthogonal and unitary vectors and can indeed represent a lot of interesting orientation features such as direction vectors ..."
Abstract - Cited by 44 (3 self) - Add to MetaCart
We are interested in regularizing fields of orthonormal vector sets, using constraint-preserving anisotropic diffusion PDE’s. Each point of such a field is defined by multiple orthogonal and unitary vectors and can indeed represent a lot of interesting orientation features such as direction vectors or orthogonal matrices (among other examples). We first develop a general variational framework that solves this regularization problem, thanks to a constrained minimization of φ-functionals. This leads to a set of coupled vector-valued PDE’s preserving the orthonormal constraints. Then, we focus on particular applications of this general framework, including the restoration of noisy direction fields, noisy chromaticity color images, estimated camera motions and DT-MRI (Diffusion Tensor MRI) datasets.

Noise removal using smoothed normals and surface fitting

by Marius Lysaker, Stanley Osher, Xue-cheng Tai - IEEE T. Image Process
"... Abstract—In this work, we use partial differential equation techniques to remove noise from digital images. The removal is done in two steps. We first use a total-variation filter to smooth the normal vectors of the level curves of a noise image. After this, we try to find a surface to fit the smoot ..."
Abstract - Cited by 39 (11 self) - Add to MetaCart
Abstract—In this work, we use partial differential equation techniques to remove noise from digital images. The removal is done in two steps. We first use a total-variation filter to smooth the normal vectors of the level curves of a noise image. After this, we try to find a surface to fit the smoothed normal vectors. For each of these two stages, the problem is reduced to a nonlinear partial differential equation. Finite difference schemes are used to solve these equations. A broad range of numerical examples are given in the paper. Index Terms—Anisotropic diffusion, image denoising, nonlinear partial differential equations (PDEs), normal processing. I.
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... our algorithm. In fact, the functional for the minimization problem we solve in the second step is identical to one of the terms in the inpainting functional of [23]. Another closely related work is =-=[24]-=-. That paper deals with minimization of constrained functionals, and in particular with -harmonic maps. For a vector valued function , they proposed an elegant way of solving with Dirichlet or Neumann...

Channel smoothing: Efficient robust smoothing of low-level signal features

by Michael Felsberg, Per-Erik Forssén, Hanno Scharr - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2006
"... In this paper, we present a new and efficient method to implement robust smoothing of low-level signal features: B-spline channel smoothing. This method consists of three steps: encoding of the signal features into channels, averaging of the channels, and decoding of the channels. We show that line ..."
Abstract - Cited by 36 (22 self) - Add to MetaCart
In this paper, we present a new and efficient method to implement robust smoothing of low-level signal features: B-spline channel smoothing. This method consists of three steps: encoding of the signal features into channels, averaging of the channels, and decoding of the channels. We show that linear smoothing of channels is equivalent to robust smoothing of the signal features if we make use of quadratic B-splines to generate the channels. The linear decoding from B-spline channels allows the derivation of a robust error norm, which is very similar to Tukey’s biweight error norm. We compare channel smoothing with three other robust smoothing techniques: nonlinear diffusion, bilateral filtering, and mean-shift filtering, both theoretically and on a 2D orientation-data smoothing task. Channel smoothing is found to be superior in four respects: It has a lower computational complexity, it is easy to implement, it chooses the global minimum error instead of the nearest local minimum, and it can also be used on nonlinear spaces, such as orientation space.
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...lts are sufficiently accurate if the normalization is applied repeatedly between sufficiently short diffusion times [39]. More theoretically sound methods make use of the Levi-Civita connection [43], =-=[44]-=- and the metric imposed by the manifold [45], but these methods are slower than component-wise diffusion and the accuracy gain is neglectable in most cases. In addition, we want to be able to weight t...

Solving variational problems and partial differential equations mapping into general target manifolds

by Facundo Mémoli, Guillermo Sapiro, Stanley Osher - J. Comput. Phys , 2004
"... ..."
Abstract - Cited by 35 (6 self) - Add to MetaCart
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...rticular flat target manifolds as the whole ���� space or as those in [37], the projection is not needed. Other authors, e.g., [8, 28], have avoided the projection step for particular cases, w=-=hile in [51]-=- the authors modify the given variational formulation, in some restricted cases, to include the projection step. 3saround. On the other hand, not all surfaces (manifolds) are originally represented in...

Variational models for image colorization via Chromaticity and Brightness decomposition

by Sung Ha Kang, Riccardo March - IEEE TRANS. IMAGE PROC , 2006
"... Colorization refers to an image processing task which recovers color of gray scale images when only small regions with color are given. We propose a couple of variational models us-ing chromaticity color component to colorize black and white images. We first consider Total Variation minimizing (TV) ..."
Abstract - Cited by 19 (1 self) - Add to MetaCart
Colorization refers to an image processing task which recovers color of gray scale images when only small regions with color are given. We propose a couple of variational models us-ing chromaticity color component to colorize black and white images. We first consider Total Variation minimizing (TV) colorization which is an extension from TV inpainting to color us-ing chromaticity model. Secondly, we further modify our model to weighted harmonic maps for colorization. This model adds edge information from the brightness data, while it recon-structs smooth color values for each homogeneous region. We introduce penalized versions of the variational models, we analyze their convergence properties, and we present numerical results including extension to texture colorization.
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...traints there are various numerical methods introduced to accurately compute non-flat features. Some mathematical analysis on S N constraints is studied in [26], and numerical methods are explored in =-=[47]-=-. In this paper, we introduce variational colorization models which use a penalty term to deal with S 2 constraint. The penalization method simplifies the computation by avoiding the direct numerical ...

A convergent and constraint-preserving finite element method for the p-harmonic flow into spheres

by John W. Barrett, Sören Bartels, Xiaobing Feng, Andreas Prohl - SIAM J. NUMER. ANAL , 2007
"... An explicit fully discrete finite element method, which satisfies the nonconvex side constraint at every node, is developed for approximating the p-harmonic flow for p 2 (1;1). Convergence of the method is established under certain conditions on the domain and mesh. Computational examples are presen ..."
Abstract - Cited by 13 (8 self) - Add to MetaCart
An explicit fully discrete finite element method, which satisfies the nonconvex side constraint at every node, is developed for approximating the p-harmonic flow for p 2 (1;1). Convergence of the method is established under certain conditions on the domain and mesh. Computational examples are presented to demonstrate finite-time blow-ups and qualitative geometric changes of weak solutions of the p-harmonic flow.

A TV-Stokes denoising algorithm

by Talal Rahman, Xue-cheng Tai, Stanley Osher
"... In this paper, we propose a two-step algorithm for denoising digital images with additive noise. Observing that the isophote directions of an image correspond to an incompressible velocity field, we impose the constraint of zero divergence on the tangential field. Combined with an energy minimizat ..."
Abstract - Cited by 8 (2 self) - Add to MetaCart
In this paper, we propose a two-step algorithm for denoising digital images with additive noise. Observing that the isophote directions of an image correspond to an incompressible velocity field, we impose the constraint of zero divergence on the tangential field. Combined with an energy minimization problem corresponding to the smoothing of tangential vectors, this constraint gives rise to a nonlinear Stokes equation where the nonlinearity is in the viscosity function. Once the isophote directions are found, an image is reconstructed that fits those directions by solving another nonlinear partial differential equation. In both steps, we use finite difference schemes to solve. We present several numerical examples to show the effectiveness of our approach.
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...Lysaker-Osher-Tai (LOT) in [11] involving a smoothing of the normal vectors ∇d0/|∇d0| in the first step, and then finding a surface to fit the smoothed normals in the second step, based on ideas from =-=[4, 2, 16]-=-. In this paper we use the same two-step approach, but we modify the first step being motivated by the observation that tangent directions to the isophote lines (lines along which the intensity is con...

Anisotropic smoothing using double orientations

by Gabriele Steidl, Tanja Teuber , 2009
"... Abstract. To improve the quality of image restoration methods direc-tional information has recently been involved in the restoration process. In this paper, we propose a two step procedure for denoising images that is particularly suited to recover sharp vertices and X junctions in the presence of h ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
Abstract. To improve the quality of image restoration methods direc-tional information has recently been involved in the restoration process. In this paper, we propose a two step procedure for denoising images that is particularly suited to recover sharp vertices and X junctions in the presence of heavy noise. In the first step, we estimate the (smoothed) orientations of the image structures, where we find the double orienta-tions at vertices and X junctions using a model of Aach et al. Based on shape preservation considerations this directional information is then applied to establish an energy functional which is minimized in the sec-ond step. We discuss the behavior of our new method in comparison with single direction approaches appearing, e.g., when using the classical structure tensor of Förstner and Gülch and demonstrate the very good performance of our method by numerical examples. 1
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...ence of heavy noise. Note that as in [16] the curvature-based method can include multiple directions. Various papers deal with the smoothing of normal vectors by minimizing certain energy functionals =-=[17, 18, 19, 20, 21, 22]-=- and use this information for subsequent denoising. In general these minimization procedures are much more expensive then our double direction approach. Kimmel, Sochen et al. suggested restoration tec...

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