A numerical algorithm for image sequence inpainting that preserves fine textures

Casaburi, Daniela; D’Amore, Luisa; Galletti, Ardelio; Marcellino, Livia

doi:10.1080/00207160.2010.537328

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…According to [3], we use a first order forward finite differences scheme for the discretization of the first space-time derivatives, and a second order central finite differences scheme for the discretization of the second space derivative. We have developed the following matrix formulation of the discretization of the PDE system in…”

Section: Numerical Approachmentioning

confidence: 99%

Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

Cuomo

Galletti

Giunta

et al. 2015

Procedia Computer Science

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In this work we present a multi-level parallel framework for the Optical Flow computation on a GPUs cluster, equipped with a scientific computing middleware (the PetSc library). Starting from a flow-driven isotropic method, which models the optical flow problem through a parabolic partial differential equation (PDE), we have designed a parallel algorithm and its software implementation that is suitable for heterogeneous computing environments (multiprocessor, single GPU and cluster of GPUs). The proposed software has been tested on real SAR images sequences. Numerical experiments highlight the performance of the proposed software framework, which can reach a gain of about 95% with respect to the sequential implementation.

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Section: Numerical Approachmentioning

confidence: 99%

Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

Cuomo

Galletti

Giunta

et al. 2015

Procedia Computer Science

Self Cite

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“…To illustrate the effectiveness of IAPG-TR, we apply it on image inpainting [3,6]. Figures 5-7 show a recovery experiment using the BRAINIX data set [19].…”

Section: Image Simulationmentioning

confidence: 99%

An inexact continuation accelerated proximal gradient algorithm for lown-rank tensor recovery

Liu

Song

2014

International Journal of Computer Mathematics

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The low n-rank tensor recovery problem is an interesting extension of the compressed sensing. This problem consists of finding a tensor of minimum n-rank subject to linear equality constraints and has been proposed in many areas such as data mining, machine learning and computer vision. In this paper, operator splitting technique and convex relaxation technique are adapted to transform the low n-rank tensor recovery problem into a convex, unconstrained optimization problem, in which the objective function is the sum of a convex smooth function with Lipschitz continuous gradient and a convex function on a set of matrices. Furthermore, in order to solve the unconstrained nonsmooth convex optimization problem, an accelerated proximal gradient algorithm is proposed. Then, some computational techniques are used to improve the algorithm. At the end of this paper, some preliminary numerical results demonstrate the potential value and application of the tensor as well as the efficiency of the proposed algorithm.

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Second-order Variational Models for Image Texture Analysis

Bergounioux

2014

Advances in Imaging and Electron Physics

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To cite this version:Maïtine Bergounioux. Second order variational models for image texture analysis. Advances in Imaging and Electron Physics, Elsevier, 2014, 181, pp.35-124 AbstractWe present variational models to perform texture analysis and/or extraction for image processing. We focus on second order decomposition models. Variational decomposition models have been studied extensively during the past decades. The most famous one is the Rudin-Osher-Fatemi model. We first recall most classical first order models . Then we deal with second order ones : we detail the mathematical framework, theoretical models and numerical implementation. We end with two 3D applications. Eventually, an appendix includes the mathematical tools that are used to perfom this study and Matlab c codes are provided.

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A numerical algorithm for image sequence inpainting that preserves fine textures

Cited by 4 publications

References 27 publications

Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

An inexact continuation accelerated proximal gradient algorithm for lown-rank tensor recovery

Second-order Variational Models for Image Texture Analysis

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