Show simple item record

dc.contributor.advisorRodríguez Valderrama, Paúl Antonioes_ES
dc.contributor.authorRojas Gómez, Renán Alfredoes_ES
dc.date.accessioned2013-03-27T20:00:03Zes_ES
dc.date.available2013-03-27T20:00:03Zes_ES
dc.date.created2012es_ES
dc.date.issued2013-03-27es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12404/4461es_ES
dc.description.abstractImage restoration consists in recovering a high quality image estimate based only on observations. This is considered an ill-posed inverse problem, which implies non-unique unstable solutions. Regularization methods allow the introduction of constraints in such problems and assure a stable and unique solution. One of these methods is Total Variation, which has been broadly applied in signal processing tasks such as image denoising, image deconvolution, and image inpainting for multiple noise scenarios. Total Variation features a regularization parameter which defines the solution regularization impact, a crucial step towards its high quality level. Therefore, an optimal selection of the regularization parameter is required. Furthermore, while the classic Total Variation applies its constraint to the entire image, there are multiple scenarios in which this approach is not the most adequate. Defining different regularization levels to different image elements benefits such cases. In this work, an optimal regularization parameter selection framework for Total Variation image restoration is proposed. It covers two noise scenarios: Impulse noise and Impulse over Gaussian Additive noise. A broad study of the state of the art, which covers noise estimation algorithms, risk estimation methods, and Total Variation numerical solutions, is included. In order to approach the optimal parameter estimation problem, several adaptations are proposed in order to create a local-fashioned regularization which requires no a-priori information about the noise level. Quality and performance results, which include the work covered in two recently published articles, show the effectivity of the proposed regularization parameter selection and a great improvement over the global regularization framework, which attains a high quality reconstruction comparable with the state of the art algorithms.es_ES
dc.description.uriTesises_ES
dc.language.isospaes_ES
dc.publisherPontificia Universidad Católica del Perúes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Perúes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/pe/es_ES
dc.sourcePontificia Universidad Católica del Perúes_ES
dc.sourceRepositorio de Tesis - PUCPes_ES
dc.subjectProcesamiento de imágenes digitaleses_ES
dc.subjectReconocimiento de imágeneses_ES
dc.subjectAlgoritmoses_ES
dc.titleAutomatic regularization parameter selection for the total variation mixed noise image restoration frameworkes_ES
dc.typeinfo:eu-repo/semantics/masterThesises_ES
thesis.degree.nameMagíster en Procesamiento de señales e imágenes digitaleses_ES
thesis.degree.levelMaestríaes_ES
thesis.degree.grantorPontificia Universidad Católica del Perú. Escuela de Posgrado.es_ES
thesis.degree.disciplineProcesamiento de señales e imágenes digitaleses_ES


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

info:eu-repo/semantics/openAccess
Except where otherwise noted, this item's license is described as info:eu-repo/semantics/openAccess