{"id":14723,"date":"2025-10-22T10:50:56","date_gmt":"2025-10-22T17:50:56","guid":{"rendered":"https:\/\/mattfife.com\/?p=14723"},"modified":"2025-07-24T11:08:33","modified_gmt":"2025-07-24T18:08:33","slug":"x-ray-backscatter-with-compressed-sensing","status":"publish","type":"post","link":"https:\/\/mattfife.com\/?p=14723","title":{"rendered":"X-ray backscatter with compressed sensing"},"content":{"rendered":"\n<p><a href=\"https:\/\/en.wikipedia.org\/wiki\/Compressed_sensing\" data-type=\"link\" data-id=\"https:\/\/en.wikipedia.org\/wiki\/Compressed_sensing\">Compressed sensing<\/a> is an image\/signal processing algorithm that allows you to re-construct an image\/signal even when you&#8217;ve lost up to 95% of the samples. It&#8217;s so good that it can even be cranked up to restore images even above what would normally be the Nyquist limit.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<span class=\"embed-youtube\" style=\"text-align:center; display: block;\"><iframe loading=\"lazy\" class=\"youtube-player\" width=\"640\" height=\"360\" src=\"https:\/\/www.youtube.com\/embed\/EuVgGrun1V0?version=3&#038;rel=1&#038;showsearch=0&#038;showinfo=1&#038;iv_load_policy=1&#038;fs=1&#038;hl=en-US&#038;autohide=2&#038;wmode=transparent\" allowfullscreen=\"true\" style=\"border:0;\" sandbox=\"allow-scripts allow-same-origin allow-popups allow-presentation allow-popups-to-escape-sandbox\"><\/iframe><\/span>\n<\/div><\/figure>\n\n\n\n<p><a href=\"https:\/\/www.youtube.com\/@AppliedScience\">Applied Science<\/a> walks through using an X-ray backscatter device to reconstruct images as near to x-ray vision as you can get at low doses. <\/p>\n\n\n\n<p>Links:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Robert Taylor <a href=\"https:\/\/humaticlabs.com\/blog\/compressed-sensing-python\/\" data-type=\"link\" data-id=\"https:\/\/humaticlabs.com\/blog\/compressed-sensing-python\/\">paper on Compression Sensing in Python<\/a>.<\/li>\n\n\n\n<li>Steve Brunton&#8217;s introduction to <a href=\"https:\/\/www.youtube.com\/watch?v=SbU1pahbbkc\" data-type=\"link\" data-id=\"https:\/\/www.youtube.com\/watch?v=SbU1pahbbkc\">compressed sensing<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Compressed sensing is an image\/signal processing algorithm that allows you to re-construct an image\/signal even when you&#8217;ve lost up to 95% of the samples. It&#8217;s so good that it can even be cranked up to restore images even above what would normally be the Nyquist limit. Applied Science walks through using an X-ray backscatter device to reconstruct images as near to x-ray vision as you can get at low doses. Links:<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[9,5],"tags":[],"class_list":["post-14723","post","type-post","status-publish","format-standard","hentry","category-cool","category-technical"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p4WECr-3Pt","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/posts\/14723","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mattfife.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=14723"}],"version-history":[{"count":1,"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/posts\/14723\/revisions"}],"predecessor-version":[{"id":14724,"href":"https:\/\/mattfife.com\/index.php?rest_route=\/wp\/v2\/posts\/14723\/revisions\/14724"}],"wp:attachment":[{"href":"https:\/\/mattfife.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14723"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mattfife.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14723"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mattfife.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}