Image sequence inpainting: Towards numerical software for detection and removal of local missing data via motion estimation

D’Amore, Luisa; Marcellino, Livia; Murli, A.

doi:10.1016/j.cam.2005.09.030

Cited by 9 publications

(12 citation statements)

References 7 publications

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“…The chosen starting point for assimilating data is been fixed as the first of August and the first of March respectively for both subdomains. As the DA problem is an inverse ill posed problem [17,18,19], a very important topic is the choice of the regularization parameters in (5) then in (6) (see Step 6 and Step 7 of Algorithm 6). Results we carried out show as the solution of the S3DVAR software depends on these parameters in terms of both accuracy (e.g.…”

Section: The S3dvar Computational Kernelmentioning

confidence: 99%

Toward the S3DVAR data assimilation software for the Caspian Sea

Arcucci¹,

Celestino²,

Toumi³

et al. 2017

AIP Conference Proceedings

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Abstract. Data Assimilation (DA) is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. The forecasting model used for producing oceanographic prediction into the Caspian Sea is the Regional Ocean Modeling System (ROMS). Here we propose the computational issues we are facing in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea with observations provided by the Group of High resolution sea surface temperature (GHRSST). We present the algorithmic strategies we employ and the numerical issues on data collected in two of the months which present the most significant variability in water temperature: August and March.

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Section: The S3dvar Computational Kernelmentioning

confidence: 99%

Toward the S3DVAR data assimilation software for the Caspian Sea

Arcucci¹,

Celestino²,

Toumi³

et al. 2017

AIP Conference Proceedings

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“…[P2] Inpainting domain detection and shape recognition: As in [10], we determine the inpainting domainB ⊂ , as follows:…”

Section: T)] I (P (T) T) + B(p (T) T) I B (P (T) T)mentioning

confidence: 99%

“…It is worth noting that in [10], the authors followed the same three basic steps. However, the main differences are in steps P1 and P2.…”

Section: T)] I (P (T) T) + B(p (T) T) I B (P (T) T)mentioning

confidence: 99%

A numerical algorithm for image sequence inpainting that preserves fine textures

Casaburi

D’Amore

Galletti

et al. 2011

International Journal of Computer Mathematics

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We describe a fast, reliable and automatic algorithm for image sequence inpainting that combines spatiotemporal interpolation with fine texture preservation inside missing areas. The algorithm provides an estimate of the inpainting error by using an automatic geometric recognition of missing regions. Computational kernels are sparse linear systems solved using Generalized Minimum RESidual iterative method equipped with AMG multigrid preconditioner. Experiments on synthetic and real data are discussed.

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“…In recent years, film restoration has attracted increasing research attention in order to provide high quality multimedia services (Kokaram, 1998;Tegolo and Isgro, 2001;D'amore et al, 2007;Kemal Gullu, 2008;Schallauer et al, 2007;Vittoria Bruni et al, 2004;Ren and Vlanchos, 2007;Maddalena, 2001).…”

Section: Introductionmentioning

confidence: 99%

Film line scratch detection using texture and shape information

Kim

2010

Pattern Recognition Letters

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Image sequence inpainting: Towards numerical software for detection and removal of local missing data via motion estimation

Cited by 9 publications

References 7 publications

Toward the S3DVAR data assimilation software for the Caspian Sea

Toward the S3DVAR data assimilation software for the Caspian Sea

A numerical algorithm for image sequence inpainting that preserves fine textures

Film line scratch detection using texture and shape information

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