{"id":13092,"date":"2014-11-19T15:53:08","date_gmt":"2014-11-19T13:53:08","guid":{"rendered":"http:\/\/hgpu.org\/?p=13092"},"modified":"2014-11-19T15:53:08","modified_gmt":"2014-11-19T13:53:08","slug":"fpga-an-efficient-and-promising-platform-for-real-time-image-processing-applications","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=13092","title":{"rendered":"FPGA: An Efficient And Promising Platform For Real-Time Image Processing Applications"},"content":{"rendered":"<p>Digital image processing(DIP) is an ever growing area with a variety of applications including medicine, video surveillance, and many more. To implement  the  upcoming  sophisticated  DIP algorithms and to process the large amount of data captured from  sources such as satellites or medical instruments,  intelligent high speed real-time systems have become imperative. Image  processing algorithms implemented in hardware (instead of software) have  recently emerged as the most viable solution for improving the performance of  image processing systems. This paper reviews the relative merit of FPGA  over  softwares  and DSPs as a platform  for  implementation of DIP applications. Our goal  is  to  familiarize applications programmers with  the state of the art in compiling high-level programs to FPGAs, and to survey  the  relevant  research work  on FPGAs. The  outstanding  features which  FPGAs  offer  such  as  optimization,  high  computational density,  low cost etc, make them an increasingly preferred choice of experts in image processing field today.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Digital image processing(DIP) is an ever growing area with a variety of applications including medicine, video surveillance, and many more. To implement the upcoming sophisticated DIP algorithms and to process the large amount of data captured from sources such as satellites or medical instruments, intelligent high speed real-time systems have become imperative. Image processing algorithms [&hellip;]<\/p>\n","protected":false},"author":615,"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":[33,3],"tags":[809,377,1786,1662],"class_list":["post-13092","post","type-post","status-publish","format-standard","hentry","category-image-processing","category-paper","tag-dsp","tag-fpga","tag-image-processing","tag-survey"],"views":2400,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13092","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\/615"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=13092"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13092\/revisions"}],"predecessor-version":[{"id":13095,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13092\/revisions\/13095"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13092"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13092"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13092"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}