{"id":12279,"date":"2014-06-14T08:35:17","date_gmt":"2014-06-14T05:35:17","guid":{"rendered":"http:\/\/hgpu.org\/?p=12279"},"modified":"2014-06-14T08:35:17","modified_gmt":"2014-06-14T05:35:17","slug":"image-denoising-using-wavelet-transform-and-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12279","title":{"rendered":"Image Denoising Using Wavelet Transform and CUDA"},"content":{"rendered":"<p>The discrete wavelet transform has a huge number of applications in science, engineering, mathematics and computer science. Most notably, it is used for signal coding to represent a discrete signal in a more redundant form, often as a preconditioning for data compression. Beginning in the 1990s, wavelets have been found to be a powerful tool for removing noise from a variety of signals (denoising). In this paper the implementation of DWT (Discrete Wavelet Transform)-based denoising algorithm in parallel manner on Graphics Processing Unit is presented, using the CUDA technology.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The discrete wavelet transform has a huge number of applications in science, engineering, mathematics and computer science. Most notably, it is used for signal coding to represent a discrete signal in a more redundant form, often as a preconditioning for data compression. Beginning in the 1990s, wavelets have been found to be a powerful tool [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,89,33,3],"tags":[1787,832,14,362,1786,20,1431],"class_list":["post-12279","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-image-processing","category-paper","tag-algorithms","tag-compression","tag-cuda","tag-discrete-wavelet-transform","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gt-630"],"views":3411,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12279","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=12279"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12279\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12279"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12279"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12279"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}