{"id":4448,"date":"2011-06-24T09:45:56","date_gmt":"2011-06-24T09:45:56","guid":{"rendered":"http:\/\/hgpu.org\/?p=4448"},"modified":"2011-06-24T09:45:56","modified_gmt":"2011-06-24T09:45:56","slug":"implementation-of-a-soft-morphological-filter-based-on-gpu-framework","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4448","title":{"rendered":"Implementation of a Soft Morphological Filter Based on GPU Framework"},"content":{"rendered":"<p>An approach to execute an improved soft morphological filter (ISMF) on Graphic Processing Unit (GPU) is present in this paper. ISMF regarded as the extension of the standard morphological operators performs well in removal of Salt-and-pepper noise and Gaussian noise simultaneously. Since a great number of data comparison and transmission, it will take a long time to run the filter. By using the software platform of Compute Unified Device Architecture (CUDA) based on GPU, ISMF can be accelerated by executing in parallel on GPU. This research takes profits of the filter&#8217;s inherently parallel feature, and realized it on GPU. Experiments demonstrated that the computing time could be significantly reduced.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>An approach to execute an improved soft morphological filter (ISMF) on Graphic Processing Unit (GPU) is present in this paper. ISMF regarded as the extension of the standard morphological operators performs well in removal of Salt-and-pepper noise and Gaussian noise simultaneously. Since a great number of data comparison and transmission, it will take a long [&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":[11,89,3],"tags":[1782,14,841,20],"class_list":["post-4448","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-filtering","tag-nvidia"],"views":1980,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4448","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=4448"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4448\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4448"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4448"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4448"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}