{"id":195773,"date":"2024-09-06T07:26:45","date_gmt":"2024-09-06T12:26:45","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2024\/09\/new-neural-framework-enhances-reconstruction-of-high-resolution-images"},"modified":"2024-09-06T07:26:45","modified_gmt":"2024-09-06T12:26:45","slug":"new-neural-framework-enhances-reconstruction-of-high-resolution-images","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2024\/09\/new-neural-framework-enhances-reconstruction-of-high-resolution-images","title":{"rendered":"New neural framework enhances reconstruction of high-resolution images"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/new-neural-framework-enhances-reconstruction-of-high-resolution-images3.jpg\"><\/a><\/p>\n<p>Deep learning (DL) has significantly transformed the field of computational imaging, offering powerful solutions to enhance performance and address a variety of challenges. Traditional methods often rely on discrete pixel representations, which limit resolution and fail to capture the continuous and multiscale nature of physical objects. Recent research from Boston University (BU) presents a novel approach to overcome these limitations.<\/p>\n<p><a href=\"https:\/\/www.spiedigitallibrary.org\/journals\/advanced-photonics-nexus\/volume-3\/issue-05\/056005\/NeuPh--scalable-and-generalizable-neural-phase-retrieval-with-local\/10.1117\/1.APN.3.5.056005.full\" target=\"_blank\">As reported<\/a> in <i>Advanced Photonics Nexus<\/i>, researchers from BU\u2019s Computational Imaging Systems Lab have introduced a local conditional neural field (LCNF) network, which they use to address the problem. Their scalable and generalizable LCNF system is known as \u201cneural phase retrieval\u201d\u2014\u201d NeuPh\u201d for short.<\/p>\n<p>NeuPh leverages advanced DL techniques to reconstruct high-resolution phase information from low-resolution measurements. This method employs a convolutional neural network (CNN)-based encoder to compress captured images into a compact latent-space representation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deep learning (DL) has significantly transformed the field of computational imaging, offering powerful solutions to enhance performance and address a variety of challenges. Traditional methods often rely on discrete pixel representations, which limit resolution and fail to capture the continuous and multiscale nature of physical objects. Recent research from Boston University (BU) presents a novel [\u2026]<\/p>\n","protected":false},"author":367,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6,8],"tags":[],"class_list":["post-195773","post","type-post","status-publish","format-standard","hentry","category-robotics-ai","category-space"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/195773","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/367"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=195773"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/195773\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=195773"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=195773"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=195773"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}