{"id":7953,"date":"2012-07-24T14:27:54","date_gmt":"2012-07-24T11:27:54","guid":{"rendered":"http:\/\/hgpu.org\/?p=7953"},"modified":"2012-07-24T14:27:54","modified_gmt":"2012-07-24T11:27:54","slug":"a-splitting-algorithm-for-directional-regularization-and-sparsification","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7953","title":{"rendered":"A Splitting Algorithm for Directional Regularization and Sparsification"},"content":{"rendered":"<p>We present a new split-type algorithm for the minimization of a p-harmonic energy with added data fidelity term. The half-quadratic splitting reduces the original problem to two straightforward problems, that can be minimized efficiently. The minimizers to the two sub-problems can typically be computed pointwise and are easily implemented on massively parallel processors. Furthermore the splitting method allows for the computation of solutions to a large number of more advanced directional regularization problems. In particular we are able to handle robust, non-convex data terms, and to define a 0-harmonic regularization energy where we sparsify directions by means of an L^0 norm.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a new split-type algorithm for the minimization of a p-harmonic energy with added data fidelity term. The half-quadratic splitting reduces the original problem to two straightforward problems, that can be minimized efficiently. The minimizers to the two sub-problems can typically be computed pointwise and are easily implemented on massively parallel processors. Furthermore the [&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":[1787,14,1786,20,378],"class_list":["post-7953","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-tesla-c2050"],"views":2173,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7953","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=7953"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7953\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7953"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7953"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7953"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}