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Region Filling and Object Removal by ExemplarBased Image Inpainting
, 2004
"... A new algorithm is proposed for removing large objects from digital images. The challenge is to fill in the hole that is left behind in a visually plausible way. In the past, this problem has been addressed by two classes of algorithms: 1) “texture synthesis” algorithms for generating large image re ..."
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Cited by 356 (1 self)
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A new algorithm is proposed for removing large objects from digital images. The challenge is to fill in the hole that is left behind in a visually plausible way. In the past, this problem has been addressed by two classes of algorithms: 1) “texture synthesis” algorithms for generating large image regions from sample textures and 2) “inpainting ” techniques for filling in small image gaps. The former has been demonstrated for “textures”—repeating twodimensional patterns with some stochasticity; the latter focus on linear “structures ” which can be thought of as onedimensional patterns, such as lines and object contours. This paper presents a novel and efficient algorithm that combines the advantages of these two approaches. We first note that exemplarbased texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a bestfirst algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual color values are computed using exemplarbased synthesis. In this paper, the simultaneous propagation of texture and structure information is achieved by a single, efficient algorithm. Computational efficiency is achieved by a blockbased sampling process. A number of examples on real and synthetic images demonstrate the effectiveness of our algorithm in removing large occluding objects, as well as thin scratches. Robustness with respect to the shape of the manually selected target region is also demonstrated. Our results compare favorably to those obtained by existing techniques.
Simultaneous Structure and Texture Image Inpainting
, 2003
"... An algorithm for the simultaneous fillingin of texture and structure in regions of missing image information is presented in this paper. The basic idea is to first decompose the image into the sum of two functions with different basic characteristics, and then reconstruct each one of these function ..."
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Cited by 220 (13 self)
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An algorithm for the simultaneous fillingin of texture and structure in regions of missing image information is presented in this paper. The basic idea is to first decompose the image into the sum of two functions with different basic characteristics, and then reconstruct each one of these functions separately with structure and texture fillingin algorithms. The first function used in the decomposition is of bounded variation, representing the underlying image structure, while the second function captures the texture and possible noise. The region of missing information in the bounded variation image is reconstructed using image inpainting algorithms, while the same region in the texture image is filledin with texture synthesis techniques. The original image is then reconstructed adding back these two subimages. The novel contribution of this paper is then in the combination of these three previously developed components, image decomposition with inpainting and texture synthesis, which permits the simultaneous use of fillingin algorithms that are suited for different image characteristics. Examples on real images show the advantages of this proposed approach.
Spacetime video completion
 in Proceedinggs of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’04
, 2004
"... We present a method for spacetime completion of large spacetime “holes ” in video sequences of complex dynamic scenes. The missing portions are filledin by sampling spatiotemporal patches from the available parts of the video, while enforcing global spatiotemporal consistency between all patc ..."
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Cited by 142 (5 self)
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We present a method for spacetime completion of large spacetime “holes ” in video sequences of complex dynamic scenes. The missing portions are filledin by sampling spatiotemporal patches from the available parts of the video, while enforcing global spatiotemporal consistency between all patches in and around the hole. This is obtained by posing the task of video completion and synthesis as a global optimization problem with a welldefined objective function. The consistent completion of static scene parts simultaneously with dynamic behaviors leads to realistic looking video sequences. Spacetime video completion is useful for a variety of tasks, including, but not limited to: (i) Sophisticated video removal (of undesired static or dynamic objects) by completing the appropriate static or dynamic background information, (ii) Correction of missing/corrupted video frames in old movies, and (iii) Synthesis of new video frames to add a visual story, modify it, or generate a new one. Some examples of these are shown in the paper. 1.
Image completion with structure propagation
 ACM Transactions on Graphics
, 2005
"... two intersecting lines (green) specified by the user, (c) intermediate result after propagating structure and texture information along the userspecified lines, and (d) final result after filling in the remaining unknown regions by texture propagation. In this paper, we introduce a novel approach t ..."
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Cited by 137 (4 self)
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two intersecting lines (green) specified by the user, (c) intermediate result after propagating structure and texture information along the userspecified lines, and (d) final result after filling in the remaining unknown regions by texture propagation. In this paper, we introduce a novel approach to image completion, which we call structure propagation. In our system, the user manually specifies important missing structure information by extending a few curves or line segments from the known to the unknown regions. Our approach synthesizes image patches along these userspecified curves in the unknown region using patches selected around the curves in the known region. Structure propagation is formulated as a global optimization problem by enforcing structure and consistency constraints. If only a single curve is specified, structure propagation is solved using Dynamic Programming. When multiple intersecting curves are specified, we adopt the Belief Propagation algorithm to find the optimal patches. After completing structure propagation, we fill in the remaining unknown regions using patchbased texture synthesis. We show that our approach works well on a number of examples that are challenging to stateoftheart techniques.
Digital inpainting based on the MumfordShahEuler image model
 European J. Appl. Math
, 2002
"... Abstract. Image inpainting is an image restoration problem, in which image models play a critical role, as demonstrated by Chan, Kang and Shen’s recent inpainting schemes based on the bounded variation [10] and the elastica [9] image models. In the present paper, we propose two novel inpainting mode ..."
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Cited by 81 (23 self)
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Abstract. Image inpainting is an image restoration problem, in which image models play a critical role, as demonstrated by Chan, Kang and Shen’s recent inpainting schemes based on the bounded variation [10] and the elastica [9] image models. In the present paper, we propose two novel inpainting models based on the MumfordShah image model [37], and its high order correction — the MumfordShahEuler image model. We also present their efficient numerical realization based on the ¡ and De Giorgi [18]. Key words. Inpainting, Bayesian, image model, Euler’s elastica, ¡
A Finite Element Method for Surface Restoration with Smooth Boundary Conditions
, 2004
"... In surface restoration usually a damaged region of a surface has to be replaced by a surface patch which restores the region in a suitable way. In particular one aims for C continuity at the patch boundary. The Willmore energy is considered to measure fairness and to allow appropriate boundar ..."
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Cited by 66 (9 self)
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In surface restoration usually a damaged region of a surface has to be replaced by a surface patch which restores the region in a suitable way. In particular one aims for C continuity at the patch boundary. The Willmore energy is considered to measure fairness and to allow appropriate boundary conditions to ensure continuity of the normal field. The corresponding L gradient flow as the actual restoration process leads to a system of fourth order partial differential equations, which can also be written as system of two coupled second order equations. As it is wellknown, fourth order problems require an implicit time discretization.
Mapping cortical change in Alzheimer’s disease, brain development, and schizophrenia
, 2004
"... ..."
Image Completion Using Efficient Belief Propagation via Priority Scheduling and Dynamic Pruning
"... In this paper, a new exemplarbased framework is presented, which treats image completion, texture synthesis and image inpainting in a unified manner. In order to be able to avoid the occurrence of visually inconsistent results, we pose all of the above imageediting tasks in the form of a discrete ..."
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Cited by 59 (0 self)
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In this paper, a new exemplarbased framework is presented, which treats image completion, texture synthesis and image inpainting in a unified manner. In order to be able to avoid the occurrence of visually inconsistent results, we pose all of the above imageediting tasks in the form of a discrete global optimization problem. The objective function of this problem is always welldefined, and corresponds to the energy of a discrete Markov Random Field (MRF). For efficiently optimizing this MRF, a novel optimization scheme, called PriorityBP, is then proposed, which carries two very important extensions over the standard Belief Propagation (BP) algorithm: “prioritybased message scheduling ” and “dynamic label pruning”. These two extensions work in cooperation to deal with the intolerable computational cost of BP, which is caused by the huge number of labels associated with our MRF. Moreover, both of our extensions are generic, since they do not rely on the use of domainspecific prior knowledge. They can therefore be applied to any MRF, i.e to a very wide class of problems in image processing and computer vision, thus managing to resolve what is currently considered as one major limitation of the Belief Propagation algorithm: its inefficiency in handling MRFs with very large discrete statespaces. Experimental results on a wide variety of input images are presented, which demonstrate the effectiveness of our imagecompletion framework for tasks such as object removal, texture synthesis, text removal and image inpainting.
Inpainting and zooming using sparse representations
 The Computer Journal
"... Representing the image to be inpainted in an appropriate sparse representation dictionary, and combining elements from Bayesian statistics and modern harmonic analysis, we introduce an expectation maximization (EM) algorithm for image inpainting and interpolation. From a statistical point of view, t ..."
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Cited by 55 (8 self)
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Representing the image to be inpainted in an appropriate sparse representation dictionary, and combining elements from Bayesian statistics and modern harmonic analysis, we introduce an expectation maximization (EM) algorithm for image inpainting and interpolation. From a statistical point of view, the inpainting/interpolation can be viewed as an estimation problem with missing data. Toward this goal, we propose the idea of using the EM mechanism in a Bayesian framework, where a sparsity promoting prior penalty is imposed on the reconstructed coefficients. The EM framework gives a principled way to establish formally the idea that missing samples can be recovered/ interpolated based on sparse representations. We first introduce an easy and efficient sparserepresentationbased iterative algorithm for image inpainting. Additionally, we derive its theoretical convergence properties. Compared to its competitors, this algorithm allows a high degree of flexibility to recover different structural components in the image (piecewise smooth, curvilinear, texture, etc.). We also suggest some guidelines to automatically tune the regularization parameter.
Inpainting Surface Holes
 In Int. Conference on Image Processing
, 2003
"... An algorithm for fillingin surface holes is introduced in this paper. The basic idea is to represent the surface of interest in implicit form, and fillin the holes with a system of geometric partial differential equations derived from image inpainting algorithms. The framework and examples with sy ..."
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Cited by 54 (3 self)
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An algorithm for fillingin surface holes is introduced in this paper. The basic idea is to represent the surface of interest in implicit form, and fillin the holes with a system of geometric partial differential equations derived from image inpainting algorithms. The framework and examples with synthetic and real data are presented.