{"id":5537,"date":"2011-09-11T11:48:13","date_gmt":"2011-09-11T08:48:13","guid":{"rendered":"http:\/\/hgpu.org\/?p=5537"},"modified":"2011-09-11T11:48:13","modified_gmt":"2011-09-11T08:48:13","slug":"translation-invariant-two-dimensional-discrete-wavelet-transform-on-graphics-processing-units","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5537","title":{"rendered":"Translation-invariant two-dimensional discrete wavelet transform on graphics processing units"},"content":{"rendered":"<p>The Discrete Wavelet Transform (DWT) is used in several signal and image processing applications. Due to the computational expense various approaches have been proposed. One approach is using graphics processing units (GPUs) as stream processors to speed up the calculation of the DWT. This paper presents a GPU implementation of the translation-invariant wavelet transform computed by the &quot;algorithme a trous&quot;. Our approach focuses on processing of infrared images, but can be easily used in different image processing applications. We extend our work by the integration of our implementation in wavelet-based edge detection and wavelet denoising. Experiments show that the computation performance could be significantly improved. Initialisation and data transfer are already existing bottlenecks, which could dramatically reduce the GPU performance, if it can&#8217;t be hided by the application.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Discrete Wavelet Transform (DWT) is used in several signal and image processing applications. Due to the computational expense various approaches have been proposed. One approach is using graphics processing units (GPUs) as stream processors to speed up the calculation of the DWT. This paper presents a GPU implementation of the translation-invariant wavelet transform computed [&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":[36,33,3],"tags":[1787,7,255,362,187,1786,20,234,182,367],"class_list":["post-5537","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-image-processing","category-paper","tag-algorithms","tag-ati","tag-ati-radeon-hd-4870","tag-discrete-wavelet-transform","tag-glsl","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gtx-280","tag-opengl","tag-shaders"],"views":2508,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5537","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=5537"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5537\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5537"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5537"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5537"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}