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Splatting the point-spread function (PSF) of every pixel is general and provides high quality, but requires prohibitive compute time. We accelerate this in two steps: In a pre-process we optimize for sparse representations of the Laplacian of all possible PSFs that we call\n            <jats:italic>spreadlets.<\/jats:italic>\n            At runtime, spreadlets can be splat efficiently to the Laplacian of an image. Integrating this image produces the final result. Our approach scales faithfully to strong motion and large out-of-focus areas and compares favorably in speed and quality with off-line and interactive approaches. It is applicable to both synthesizing from pinhole as well as reconstructing from stochastic images, with or without layering.\n          <\/jats:p>","DOI":"10.1145\/3197517.3201379","type":"journal-article","created":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T15:56:23Z","timestamp":1533052583000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Laplacian kernel splatting for efficient depth-of-field and motion blur synthesis or reconstruction"],"prefix":"10.1145","volume":"37","author":[{"given":"Thomas","family":"Leimk\u00fchler","sequence":"first","affiliation":[{"name":"MPI Informatik, Saarland Informatics Campus, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans-Peter","family":"Seidel","sequence":"additional","affiliation":[{"name":"MPI Informatik, Saarland Informatics Campus, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Ritschel","sequence":"additional","affiliation":[{"name":"University College London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,7,30]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proc. 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