{"id":5852,"date":"2011-10-10T13:17:23","date_gmt":"2011-10-10T10:17:23","guid":{"rendered":"http:\/\/hgpu.org\/?p=5852"},"modified":"2011-10-10T13:17:23","modified_gmt":"2011-10-10T10:17:23","slug":"denoising-volumetric-data-on-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5852","title":{"rendered":"Denoising Volumetric Data on GPU"},"content":{"rendered":"<p>Volumetric data is currently gradually being used more and more in everyday aspect of our lives. Processing such data is computationally expensive and until now more sophisticated algorithms could not be used. The possibilities of processing such data have considerably widened since the increase of parallel computational power in modern GPUs. We present a novel scheme for running a nonlocal means denoising algorithm on a commodity-grade GPU. The speedup is considerable, shortening the time needed for denoise one abdominal CT scan in minutes instead of hours without compromising the result quality. Such approach allows for example lowering the radiation doses for patients being examined with a CT scan.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Volumetric data is currently gradually being used more and more in everyday aspect of our lives. Processing such data is computationally expensive and until now more sophisticated algorithms could not be used. The possibilities of processing such data have considerably widened since the increase of parallel computational power in modern GPUs. We present a novel [&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":[36,33,38,90,3],"tags":[1787,1786,1788,20,226,373,1015,1793,860,567],"class_list":["post-5852","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-image-processing","category-medicine","category-opencl","category-paper","tag-algorithms","tag-image-processing","tag-medicine","tag-nvidia","tag-nvidia-geforce-8800-gt","tag-nvidia-geforce-gtx-275","tag-nvidia-geforce-gtx-460","tag-opencl","tag-signal-denoising","tag-tomography"],"views":2250,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5852","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=5852"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5852\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5852"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5852"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5852"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}