{"id":5107,"date":"2011-08-15T16:07:58","date_gmt":"2011-08-15T13:07:58","guid":{"rendered":"http:\/\/hgpu.org\/?p=5107"},"modified":"2011-08-18T21:25:11","modified_gmt":"2011-08-18T18:25:11","slug":"robust-non-local-denoising-of-colored-depth-data","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5107","title":{"rendered":"Robust non-local denoising of colored depth data"},"content":{"rendered":"<p>We give a brief discussion of denoising algorithms for depth data and introduce a novel technique based on the NL-means filter. A unified approach is presented that removes outliers from depth data and accordingly achieves an unbiased smoothing result. This robust denoising algorithm takes intra-patch similarity and optional color information into account in order to handle strong discontinuities and to preserve fine detail structure in the data. We achieve fast computation times with a GPU-based implementation. Results using data from a time-of-flight camera system show a significant gain in visual quality.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We give a brief discussion of denoising algorithms for depth data and introduce a novel technique based on the NL-means filter. A unified approach is presented that removes outliers from depth data and accordingly achieves an unbiased smoothing result. This robust denoising algorithm takes intra-patch similarity and optional color information into account in order to [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,89,33,3],"tags":[1787,14,1786,20,183,860],"class_list":["post-5107","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-image-processing","category-paper","tag-algorithms","tag-cuda","tag-image-processing","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-signal-denoising"],"views":2381,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5107","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5107"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5107\/revisions"}],"predecessor-version":[{"id":5203,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5107\/revisions\/5203"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5107"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5107"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5107"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}