{"id":9384,"date":"2013-05-16T22:59:14","date_gmt":"2013-05-16T19:59:14","guid":{"rendered":"http:\/\/hgpu.org\/?p=9384"},"modified":"2013-05-16T22:59:14","modified_gmt":"2013-05-16T19:59:14","slug":"point-spread-function-estimation-of-solar-surface-images-with-a-cooperative-particle-swarm-optimization-on-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9384","title":{"rendered":"Point Spread Function Estimation of Solar Surface Images with a Cooperative Particle Swarm Optimization on GPUs"},"content":{"rendered":"<p>We present a method for estimating the point spread function (PSF) of solar surface images acquired from ground telescopes and degraded by atmosphere. The estimation is done by retrieving the wavefront phase using a set of short exposures, the speckle reconstruction of the observed object and a PSF model parametrized by Zernike polynomials. Estimates of the wavefront phase and PSF are computed by minimizing an error function with a cooperative particle swarm optimization method, implemented in OpenCL to take advantage of highly parallel GPUs. A calibration method is presented to adjust the algorithm parameters for low cost results, providing solid estimations for either low frequency and high frequency images. Results show that the method has a fast convergence and is robust to noise degradation. Experiments run on a NVidia Tesla C2050 were able to compute 100 PSFs with 50 Zernike polynomials in ~36 minutes. The algorithm is also not affected by the number of Zernikes used, i.e., execution time increased only 17% when the number of Zernike coeffcients increased tenfold, from 50 to 500.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a method for estimating the point spread function (PSF) of solar surface images acquired from ground telescopes and degraded by atmosphere. The estimation is done by retrieving the wavefront phase using a set of short exposures, the speckle reconstruction of the observed object and a PSF model parametrized by Zernike polynomials. Estimates of [&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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,96,33,90,3],"tags":[1787,1794,1786,20,1793,176,1342,378,390],"class_list":["post-9384","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-astrophysics","category-image-processing","category-opencl","category-paper","tag-algorithms","tag-astrophysics","tag-image-processing","tag-nvidia","tag-opencl","tag-package","tag-particle-swarm-optimization","tag-tesla-c2050","tag-thesis"],"views":2280,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9384","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=9384"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9384\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9384"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9384"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9384"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}