{"id":6223,"date":"2011-11-09T15:52:16","date_gmt":"2011-11-09T13:52:16","guid":{"rendered":"http:\/\/hgpu.org\/?p=6223"},"modified":"2011-11-09T15:52:16","modified_gmt":"2011-11-09T13:52:16","slug":"performance-tuning-for-cuda-accelerated-neighborhood-denoising-filters","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6223","title":{"rendered":"Performance Tuning for CUDA-Accelerated Neighborhood Denoising Filters"},"content":{"rendered":"<p>Neighborhood  denoising  filters are powerful techniques in image processing and can  effectively enhance the  image quality in CT reconstructions. In this study, by taking the  bilateral filter and the non-local mean filter as two examples, we  discuss their implementations and perform fine-tuning on the targeted  GPU architecture.  Experimental results  show that the straightforward GPU-based neighborhood filters can be further accelerated by pre-fetching. The optimized GPU-accelerated denoising filters are ready for plug-in into reconstruction framework to enable fast denoising without compromising image quality.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Neighborhood denoising filters are powerful techniques in image processing and can effectively enhance the image quality in CT reconstructions. In this study, by taking the bilateral filter and the non-local mean filter as two examples, we discuss their implementations and perform fine-tuning on the targeted GPU architecture. Experimental results show that the straightforward GPU-based neighborhood [&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":[89,33,3],"tags":[479,478,14,841,1786,20,379,67,860],"class_list":["post-6223","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-paper","tag-computed-tomography","tag-ct","tag-cuda","tag-filtering","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-performance","tag-signal-denoising"],"views":2320,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6223","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=6223"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6223\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6223"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6223"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6223"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}