Retinex is an image restoration method and the center\/surround Retinex is appropriate for parallelization because it utilizes a convolution operation with large kernel size to achieve dynamic range compression and color\/lightness rendition. However, its great capability for image enhancement comes with intensive computation. This paper presents a GPURetinex, which is a data parallel algorithm based on GPGPU\/CUDA. The GPURetinex exploits GPGPU’s massively parallel architecture and hierarchical memory to improve efficiency. The GPURetinex has been further improved by optimizing the memory usage and out-of-boundary extrapolation in the convolution step. In our experiments, the GPURetinex can gain 72 times speedup compared with the optimized single-threaded CPU implementation by OpenCV for the images with 2048 x 2048 resolution. The proposed method also outperforms a Retinex implementation based on the NPP (nVidia Performance Primitives).<\/p>\n","protected":false},"excerpt":{"rendered":"
Retinex is an image restoration method and the center\/surround Retinex is appropriate for parallelization because it utilizes a convolution operation with large kernel size to achieve dynamic range compression and color\/lightness rendition. However, its great capability for image enhancement comes with intensive computation. This paper presents a GPURetinex, which is a data parallel algorithm based […]<\/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":[1787,832,14,1786,20],"class_list":["post-5294","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-image-processing","tag-nvidia"],"views":2467,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5294","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=5294"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5294\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5294"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5294"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5294"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}