Weighted and extended total variation for image restoration and decomposition

Hamidi, Abdallah El; Ménard, Michel; Lugiez, Mathieu; Ghannam, Clara

doi:10.1016/j.patcog.2009.10.011

Cited by 36 publications

(18 citation statements)

References 22 publications

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“…Many methods have been proposed for staircasing reduction, e.g. WTV and adaptive TV methods [12], high order methods [4], Tikhonov nonlocal TV method [21], and combined models [25]. Here, we propose a simple regularization functional…”

Section: A Weighted H 1 Seminorm Regularization Methodsmentioning

confidence: 99%

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A weightedH¹seminorm regularization method for Fredholm integral equations of the first kind

Lin

Yang

2013

International Journal of Computer Mathematics

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Many problems in mathematics and engineering lead to Fredholm integral equations of the first kind, e.g. signal and image processing. These kinds of equations are difficult to solve numerically since they are illposed. Therefore, regularization is required to obtain a reasonable approximate solution. This paper presents a new regularization method based on a weighted H 1 seminorm. Details of numerical implementation are given. Numerical examples, including one-dimensional and two-dimensional integral equations, are presented to illustrate the efficiency of the proposed approach. Numerical results show that the proposed regularization method can restore edges as well as details.

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Section: A Weighted H 1 Seminorm Regularization Methodsmentioning

confidence: 99%

“…In order to compensate for staircase effect and loss of details, many methods have been proposed, e.g. weighted TV (WTV) and adaptive TV methods [3,9,10,12,30], high order methods [4,8], Tikhonov nonlocal TV method [21], and combined models [25]. Some methods focus on image denoising [3,4,8,24,29,34].…”

Section: Introductionmentioning

confidence: 99%

A weightedH¹seminorm regularization method for Fredholm integral equations of the first kind

Lin

Yang

2013

International Journal of Computer Mathematics

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“…Those inter-plane motions create a turbulent and changing dynamic texture, which is not characterized by global motion. In order to obtain a robust and reliable motion estimation, an extended Meyer decomposition [4] is applied, which decomposes the image into three components, containing geometrical, textural, and noise information. Based on the geometrical component, the optical flow is estimated using a partial derivative equation (Lucas Kanade), and mean (4 cycles) flow velocity fields were computed [5].…”

Section: Motion Estimation For Echopiv Image Sequencesmentioning

confidence: 99%

Vortex Tracking in High Density Vector Fields

Guerin

Gomez‐Krämer

Ménard

et al. 2012

Lecture Notes in Computer Science

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Abstract. Echo Particular Velocity Imaging (EchoPIV) has become a promising tool to detect flow patterns in cardiovascular structures. We present in this paper a new method for the detection and tracking of the vortex in the left ventricle for EchoPIV image sequences. The vortex is roughly localized by approximating an affine motion model to the estimated high density motion vector field and analyzing the significance of its rotational component in a statistical test. Based on a detection of the center of rotation and the approximated motion model, the area of the vortex is segmented. False positives are eliminated by analyzing the vortex's shape. Results on synthetic and real data are promising.

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“…We have developed an approach in [4] to simplify and to extract shapes from images with lines. The process of shapes extraction thus relies on a three step process: 1) Firstly, the lettrine's images are simplified using a decomposition which relies on a series of projection (see [8], [9] for complete explanations). An example of result can be observed in figure 3).…”

Section: A Region Extractionmentioning

confidence: 99%

Using Ontologies to Reduce the Semantic Gap between Historians and Image Processing Algorithms

Coustaty

Bouju

Bertet

et al. 2011

2011 International Conference on Document Analysis and Recognition

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Abstract-To reduce the gap between pixel data and thesaurus semantics, this paper presents a novel approach using mapping between two ontologies on images of drop-capitals (also named dropcaps or lettrines): In the first ontology, each dropcap image is endowed with semantic information describing its content. It is generated from a database of lettrines images -namely Ornamental Letter Images DataBase -manually populated by historians with dropcap images annotations. For the second ontology we have developed image processing algorithms to extract image regions on the basis of a number of features. These features, as well as spatial relations, among regions form the basis of the ontology. The ontologies are then enriched by inference rules to annotate some regions to automatically deduce their semantics. In this article, the method is presented together with preliminary experimental results and an illustrative example.

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Weighted and extended total variation for image restoration and decomposition

Cited by 36 publications

References 22 publications

A weightedH¹seminorm regularization method for Fredholm integral equations of the first kind

A weightedH¹seminorm regularization method for Fredholm integral equations of the first kind

Vortex Tracking in High Density Vector Fields

Using Ontologies to Reduce the Semantic Gap between Historians and Image Processing Algorithms

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Weighted and extended total variation for image restoration and decomposition

Cited by 36 publications

References 22 publications

A weightedH1seminorm regularization method for Fredholm integral equations of the first kind

A weightedH1seminorm regularization method for Fredholm integral equations of the first kind

Vortex Tracking in High Density Vector Fields

Using Ontologies to Reduce the Semantic Gap between Historians and Image Processing Algorithms

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Product

Resources

About

A weightedH¹seminorm regularization method for Fredholm integral equations of the first kind

A weightedH¹seminorm regularization method for Fredholm integral equations of the first kind