{"id":6787,"date":"2022-06-11T09:20:39","date_gmt":"2022-06-11T07:20:39","guid":{"rendered":"https:\/\/www.ibidem-translations.com\/edu\/?p=6787"},"modified":"2022-07-21T13:28:24","modified_gmt":"2022-07-21T11:28:24","slug":"traduccion-swinai-inteligencia-artificial-imagenes-mejora","status":"publish","type":"post","link":"https:\/\/www.ibidem-translations.com\/edu\/traduccion-swinai-inteligencia-artificial-imagenes-mejora\/","title":{"rendered":"SwinIR, la IA que multiplica por 8 la nitidez de fotograf\u00edas faciales borrosas"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\" style=\"text-align:center\"><span style=\"color:#0693e3\" class=\"color\"> A Spanish translation reviewed &amp; postedited by In\u00e9s, a Spain-based translation student specializing in <\/span><a href=\"https:\/\/www.ibidem-translations.com\/spanish.php\"><span style=\"color:#0693e3\" class=\"color\">English to Spanish translations<\/span><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"text-align:center\"><strong><span style=\"color:#0693e3\" class=\"color\">An original text written by<\/span><\/strong><span style=\"color:#0693e3\" class=\"color\"> <\/span><strong><span style=\"color:#0693e3\" class=\"color\">Louis Bouchard,&nbsp;originally published in<\/span><\/strong><span style=\"color:#0693e3\" class=\"color\"> <br><\/span><a rel=\"noreferrer noopener\" href=\"https:\/\/www.louisbouchard.ai\/swinir\/\" target=\"_blank\"><span style=\"color:#0693e3\" class=\"color\">https:\/\/www.louisbouchard.ai\/swinir\/<\/span><\/a><span style=\"color:#0693e3\" class=\"color\"> <\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"text-align:center\">* * *<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u00a1Convierte tus im\u00e1genes de 512 px  <br>a 4K gracias a la Inteligencia Artificial!<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"watch-the-video\">Ver v\u00eddeo<\/h3>\n\n\n\n<figure class=\"wp-block-embed-youtube aligncenter wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"This AI makes blurry faces look 8 times sharper! SwinIR: Photo Upsampling\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/GFm3RfrtDoU?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">\u00bfAlguna vez te ha pasado que hab\u00eda una foto que te gustaba mucho, pero de la que s\u00f3lo ten\u00edas una versi\u00f3n min\u00fascula, como la de esta imagen de arriba a la izquierda? \u00bfNo ser\u00eda genial poder coger esa imagen y hacer que se viera el doble de bien? Suena bien, pero \u00bfqu\u00e9 pasar\u00eda si pudieras mejorarla 4 o incluso 8 veces m\u00e1s? Eso s\u00ed que ser\u00eda genial. Pues hoy vamos a ver c\u00f3mo mejorar la resoluci\u00f3n de una imagen un 400%, logrando cuatro veces m\u00e1s p\u00edxeles de alto y ancho, m\u00e1s detalles, y mayor nitidez. Lo mejor es que esto se consigue en unos pocos segundos, de forma completamente autom\u00e1tica, y funciona con pr\u00e1cticamente cualquier imagen. Ah, y puedes hacerlo t\u00fa mismo con una versi\u00f3n de demo que han puesto a disposici\u00f3n de todo el mundo, como veremos en el video.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><a href=\"http:\/\/eepurl.com\/huGLT5\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*OC8eGXqTgUGtXCgE.png\" alt=\"\"\/><\/a><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"image-upsampling\">Aumento del n\u00famero de p\u00edxeles de una imagen (image upsampling)<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Antes de profundizar en este incre\u00edble modelo, tenemos que explicar  el concepto de sobremuestreo de fotos o superresoluci\u00f3n de imagen. El objetivo aqu\u00ed es construir una imagen de alta resoluci\u00f3n a partir de una imagen de baja resoluci\u00f3n, que en este caso es una cara, pero podr\u00eda ser cualquier objeto, animal o paisaje. La imagen de baja resoluci\u00f3n tendr\u00e1 512 p\u00edxeles o menos; no ser\u00e1 muy borrosa, pero claramente quedar\u00e1 lejos de una imagen en alta definici\u00f3n, sobre todo si la ponemos a pantalla completa.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*0JbMIfGK4WbB3mR6GcZBwg.png\" alt=\"\"\/><figcaption>Ejemplo de imagen de baja definici\u00f3n.<\/figcaption><\/figure><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">M\u00edrala en detalle. Cogemos esta imagen de baja definici\u00f3n y la transformamos en una imagen de alta definici\u00f3n. mucho m\u00e1s n\u00edtida. En este caso, una imagen de 2048 p\u00edxeles cuadrados, que es cuatro veces m\u00e1s HD.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*0JbMIfGK4WbB3mR6GcZBwg.png\" alt=\"\"\/><figcaption>Imagen mejorada usando SwinIR.<\/figcaption><\/figure><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Para lograr eso, generalmente se usa una arquitectura tipo UNet con redes neuronales convolucionales, que expliqu\u00e9 en art\u00edculos anteriores, y que te invito a leer si deseas obtener m\u00e1s informaci\u00f3n. El principal inconveniente es que las CNNs tienen dificultades para adaptarse a conjuntos de datos extremadamente amplios, ya que utilizan los mismos n\u00facleos para todas las im\u00e1genes, lo que las hace excelentes para resultados locales y generales, pero menos potentes para resultados globales cuando queremos obtener resultados \u00f3ptimos para cada imagen individual.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*8GPsf622HvOTyEoBGyhYlw.gif\" alt=\"\"\/><figcaption>Convoluciones vs autoatenci\u00f3n utilizadas en transformadores.<\/figcaption><\/figure><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Los Transformers son un tipo de arquitectura muy prometedora gracias a su mecanismo de autoatenci\u00f3n que captura interacciones globales entre contextos para cada imagen, pero requieren c\u00e1lculos muy complejos, \u200b\u200bno adecuados para im\u00e1genes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Aqu\u00ed, en lugar de usar CNN o Transformers, crearon una arquitectura similar a UNet con mecanismos de convoluci\u00f3n y atenci\u00f3n. O, m\u00e1s en concreto, utilizando el Transformer Swin. El Transformer Swin es asombroso ya que tiene la ventaja de las CNNs a la hora de procesar im\u00e1genes de mayor tama\u00f1o y prepararlas para los mecanismos de atenci\u00f3n. Y estos mecanismos de atenci\u00f3n crear\u00e1n conexiones de largo alcance para que el modelo comprenda mucho mejor la imagen general al final y tambi\u00e9n pueda recrear la imagen mucho mejor. No entrar\u00e9 en detalles sobre el <a href=\"https:\/\/pub.towardsai.net\/will-transformers-replace-cnns-in-computer-vision-55657a196833\">Transformer Swin<\/a> puesto que ya analic\u00e9 esta arquitectura hace unos meses, explicando sus diferencias con las CNNs y las arquitecturas de Transformer cl\u00e1sicas utilizadas en el procesamiento del lenguaje natural.<br>Si deseas obtener m\u00e1s informaci\u00f3n al respecto y entender c\u00f3mo los investigadores aplicaron Transformers a la Visi\u00f3n Artificial, consulta <a href=\"https:\/\/pub.towardsai.net\/will-transformers-replace-cnns-in-computer-vision-55657a196833\">mi art\u00edculo &#8220;Towards AI&#8221;<\/a> y vuelve aqu\u00ed luego para leer la explicaci\u00f3n de este modelo de mejora de calidad de im\u00e1genes.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"swinir\">SwinIR<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">El modelo SwinIR puede realizar muchas tareas con im\u00e1genes, incluyendo la mejora de resoluci\u00f3n (upsampling). Como he mencionado, usa circunvoluciones, lo que permite im\u00e1genes m\u00e1s grandes. En concreto, utiliza una capa convolucional para reducir el tama\u00f1o de la imagen, como se observa a continuaci\u00f3n.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*KVY_iG1OGSYNx_PtbGjYog.png\" alt=\"\"\/><figcaption>Arquitectura de SwinIR para la restauraci\u00f3n de im\u00e1genes. Ilustraci\u00f3n de <a rel=\"noreferrer noopener\" href=\"https:\/\/arxiv.org\/pdf\/2108.10257.pdf\" target=\"_blank\">SwinIR [1]<\/a>&nbsp;.<\/figcaption><\/figure><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">La imagen reducida se env\u00eda al modelo y tambi\u00e9n pasa directamente al m\u00f3dulo de reconstrucci\u00f3n para obtener informaci\u00f3n general. Esta representaci\u00f3n tendr\u00e1 el aspecto de m\u00faltiples versiones extra\u00f1as y borrosas de la imagen, lo que aportar\u00e1 informaci\u00f3n valiosa al m\u00f3dulo de mejora sobre c\u00f3mo deber\u00eda verse la imagen principal. Luego podemos observar las capas del Transformer Swin acopladas con convoluciones. Esto sirve para comprimir a\u00fan m\u00e1s la imagen y extraer informaci\u00f3n precisa sobre el estilo y los detalles, dejando de lado la imagen general. `Por esta raz\u00f3n se vuelve a a\u00f1adir la imagen convolucionada para obtener la informaci\u00f3n general que falta con una conexi\u00f3n de salto.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><a href=\"https:\/\/www.louisbouchard.ai\/learnai\/\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*trlxHefylCBKNJku.png\" alt=\"\"\/><\/a><\/figure><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Finalmente, todo esto se env\u00eda a un m\u00f3dulo de reconstrucci\u00f3n llamado Subp\u00edxel, que utiliza tanto las caracter\u00edsticas generales m\u00e1s grandes como las caracter\u00edsticas detalladas m\u00e1s peque\u00f1as que acabamos de crear para reconstruir una imagen de mayor definici\u00f3n. Puedes imaginar esto como una red neuronal convolucional pero al rev\u00e9s, o simplemente como un decodificador, que recoge las caracter\u00edsticas condensadas originales y reconstruye una imagen m\u00e1s grande a partir de ellas. De nuevo, si deseas obtener m\u00e1s informaci\u00f3n sobre las CNNs y los decodificadores, consulta algunos de los art\u00edculos que publiqu\u00e9 sobre ellos.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Por lo tanto, se env\u00eda la imagen a una CNN, donde se crea una representaci\u00f3n que ser\u00e1 utilizada m\u00e1s tarde, y a la arquitectura del Transformer Swin, para comprimir a\u00fan m\u00e1s la informaci\u00f3n y analizar las caracter\u00edsticas m\u00e1s importantes cuya resoluci\u00f3n se pretende mejorar. Despu\u00b4\u00e9s se integran todos los datos y se reconstruye la versi\u00f3n de alta definici\u00f3n mediante un decodificador.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00a1Y voil\u00e1!<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Solo necesitas un volumen adecuado de datos para obtener resultados tan incre\u00edbles como este (\u00a1m\u00edralo en <a href=\"https:\/\/youtu.be\/GFm3RfrtDoU\">este video<\/a> !).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Por supuesto, como en cualquier otra cosa, existen algunas limitaciones&#8230; <br>En este caso, probablemente debido a la capa convolucional inicial, este m\u00e9todo no funciona bien con im\u00e1genes muy peque\u00f1as (de menos de 200 p\u00edxeles de ancho) y puede que aparezcan cosas raras y resultados extra\u00f1os&#8230;<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*REF2oTYnMt0kJBswIqrSWw.png\" alt=\"\"\/><figcaption>Versi\u00f3n mejorada de una imagen de 175 x 183 con SwinIR.<\/figcaption><\/figure><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Parece que tambi\u00e9n se puede usar esta tecnolog\u00eda para eliminar las arrugas&#8230; Fuera de eso, los resultados son bastante locos, pero tras haber jugado mucho con \u00e9l en los \u00faltimos d\u00edas, creo que la mejora de X4 es incre\u00edble.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00a1T\u00fa tambi\u00e9n puedes jugar con \u00e9l! El repositorio de GitHub est\u00e1 disponible para todo el mundo y en \u00e9l se encuentran modelos preentrenados e incluso una demo con la que puedes probarlo sin necesidad de introducir ning\u00fan c\u00f3digo. Por supuesto, esto ha sido solo una descripci\u00f3n general de este incre\u00edble nuevo modelo, as\u00ed que te invito encarecidamente a leer el informe completo para una comprensi\u00f3n t\u00e9cnica m\u00e1s profunda. Todos los enlaces est\u00e1n en las referencias a continuaci\u00f3n. \u00a1Cu\u00e9ntame qu\u00e9 te ha parecido, espero que hayas disfrutado la lectura!<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00a1Gracias una vez m\u00e1s a&nbsp;<a href=\"https:\/\/wandb.ai\/site\">Weights &amp; Biases<\/a>&nbsp;por patrocinar el video y el art\u00edculo y a todos los que todav\u00eda est\u00e1n leyendo!<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00a1Nos vemos la pr\u00f3xima semana con otro art\u00edculo interesante!<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Si te gusta mi trabajo y quieres estar al d\u00eda con la IA, s\u00edgueme en mis otras cuentas de redes sociales (<a href=\"https:\/\/www.linkedin.com\/in\/whats-ai\/\">LinkedIn<\/a>,&nbsp;<a href=\"https:\/\/twitter.com\/Whats_AI\">Twitter<\/a>) y suscr\u00edbete a mi <a href=\"http:\/\/eepurl.com\/huGLT5\"><strong>bolet\u00edn<\/strong><\/a> semanal de IA.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"to-support-me\">Para apoyarme:<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li> La mejor manera de apoyarme es siendo miembro de este sitio web o suscribi\u00e9ndote a mi canal en <strong>YouTube<\/strong> si te gusta el formato video.<\/li><li>S\u00edgueme aqu\u00ed o en&nbsp;<a href=\"https:\/\/whats-ai.medium.com\/\"><strong>Medium<\/strong><\/a><\/li><li>\u00bfQuieres aprender sobre IA o mejorar tus conocimientos?  <a href=\"https:\/\/www.louisbouchard.ai\/learnai\/\">Lee esto<\/a>&nbsp;!<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"references\">Referencias<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li>Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L. y Timofte, R., 2021. SwinIR: restauraci\u00f3n de im\u00e1genes con un transformer swin. Actas de la Conferencia Internacional IEEE\/CVF sobre Visi\u00f3n Artificial (p\u00e1gs. 1833\u20131844).<\/li><li>C\u00f3digo:&nbsp;<a href=\"https:\/\/github.com\/JingyunLiang\/SwinIR\">https:\/\/github.com\/JingyunLiang\/SwinIR<\/a><\/li><li>Demo: <a href=\"https:\/\/replicate.ai\/jingyunliang\/swinir\">https:\/\/replicate.ai\/jingyunliang\/swinir<\/a><\/li><\/ul>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-right kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;right&quot;,&quot;id&quot;:&quot;6787&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;Valora este art\u00edculo&quot;,&quot;legend&quot;:&quot;0\\\/5 - (0 votes)&quot;,&quot;size&quot;:&quot;24&quot;,&quot;title&quot;:&quot;SwinIR, la IA que multiplica por 8 la nitidez de fotograf\u00edas faciales borrosas&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} - ({count} {votes})&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 0px;\">\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 19.2px;\">\n            <span class=\"kksr-muted\">Valora este art\u00edculo<\/span>\n    <\/div>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"Translation into Spanish of an interesting article by  Louis Bouchard, AI advocate, writter, and speaker. Co-founder &#038; Head of Community at Towards AI, Louis focuses on making AI accessible, sharing and explaining artificial intelligence terms and news the best way possible in order to demystify the AI \u201cblack box\u201d and sensitize people about the risks of using it.","protected":false},"author":10,"featured_media":6900,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[80],"tags":[300,307,263,306],"class_list":["post-6787","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-translations-spanish","tag-ai","tag-image-upscaling","tag-spanish-translation","tag-swinai"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"Translation into Spanish of an interesting article by Louis Bouchard, AI advocate, writter, and speaker. 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