{"id":79985,"date":"2018-03-09T23:25:54","date_gmt":"2018-03-09T23:25:54","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/image-nand-film-denoising-by-mon-local-means\/"},"modified":"2018-03-09T23:25:54","modified_gmt":"2018-03-09T23:25:54","slug":"image-nand-film-denoising-by-mon-local-means","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/image-nand-film-denoising-by-mon-local-means\/","title":{"rendered":"Image nand film denoising by mon-local means"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Antonio Buades Capo <\/strong><\/h2>\n<p>El principal foco de esta tesis es, primero, definir un m\u00e9todo matem\u00e1tico y una metodolog\u00eda experimental para comparar y clasificar los m\u00e9todos de eliminaci\u00f3n de ruido en im\u00e1genes digitales. segundo, proponer un algoritmo (non-local means), capaz de preservar los detalles de la imagen durante el proceso de eliminaci\u00f3n del ruido. la comparaci\u00f3n de que todos los detalles de la imagen original sean preservados se puede realizar calculando  la diferencia entre la imagen degradada y la versi\u00f3n restaurada. Esta diferencia no deber\u00eda contener ninguna estructura o caracter\u00edsticas visibles y deber\u00eda parecerse lo m\u00e1s posible a un ruido. Esta diferencia se ha llamado \u00abmethod noise\u00bb y se ha comparado matem\u00e1ticamente y visualmente para los principales m\u00e9todos de eliminaci\u00f3n del ruido. tambi\u00e9n proponemos aplicar los algoritmos de restauraci\u00f3n a ruido puro. Esta nueva imagen deber\u00eda seguir pareci\u00e9ndose a un ruido puro con una magnitud de las oscilaciones menor. La creaci\u00f3n de estructura a partir de ruido significa la creaci\u00f3n de artefactos cuando el algoritmo se aplique a una imagen real. segundo, hemos propuesto un algoritmo (non-local means), fijandonos en la imagen misma y sin hacer ning\u00fan tipo de hip\u00f3tesis sobre su regularidad. Este nuevo algoritmo reemplaza el valor de un pixel por una media ponderada de todos los p\u00edxeles de la imagen.  los pesos son oscilaciones y conserva toda la informaci\u00f3n ya que se hace una media solo delos pixeles  similares. Por tanto, el nl-means usa auto-similitudes de la imagen para reducir el ruido. el algoritmo nl-means esta particularmente bien adaptado a las secuencias de imagen. Durante mucho tiempo los investigadores en restauraci\u00f3n de v\u00eddeo han restaurado cada p\u00edxel siguiendo su trayectoria a trav\u00e9s de la secuencia. Este hecho combina la restauraci\u00f3n est\u00e1tica con la compensaci\u00f3n del movimiento. Sin embargo, hasta el momento nadie ha conseguido dise\u00f1ar un algoritmo capaz de seguir de manera efectiva las<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Image nand film denoising by mon-local means<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Image nand film denoising by mon-local means <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Antonio Buades Capo <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Illes balears<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 19\/05\/2006<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Michel Morel Jean<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Luis \u00e1lvarez le\u00f3n <\/li>\n<li>albert Cohen (vocal)<\/li>\n<li>bernard Roug\u00e9 (vocal)<\/li>\n<li> Lisani roca Jos\u00e9 Luis (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Antonio Buades Capo El principal foco de esta tesis es, primero, definir un m\u00e9todo matem\u00e1tico y una [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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