{"id":182835,"date":"2007-09-21T00:00:00","date_gmt":"2009-10-31T10:04:41","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/variable-aperture-photography\/"},"modified":"2016-09-09T09:52:37","modified_gmt":"2016-09-09T16:52:37","slug":"variable-aperture-photography","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/variable-aperture-photography\/","title":{"rendered":"Variable-Aperture Photography"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this talk I will describe three projects that harness the potential of variable-aperture photography \u2013 capturing multiple photos by manipulating basic lens controls such as aperture and focus. I will show that by combining such photos, the information encoded in defocus can be used to achieve a variety of goals. First, I will describe a new method for computing highly detailed 3D shape by controlling both the aperture and focus of a lens. This method is particularly well-suited for scenes with high geometric complexity, for which standard computer vision techniques can break down. Second, I will show that we can exploit &#8220;aperture bracketing&#8221; \u2013 a one-button operation on most digital SLR&#8217;s \u2013 to allow refocusing and other effects in post-capture, all with increased dynamic range. To achieve this, we compute a layered scene model which simultaneously accounts for defocus, high dynamic range exposure, and noise in the input images. Finally, I will talk about our current work on &#8220;light-efficient&#8221; photography, whose goal is to capture photos with the desired depth-of-field in the shortest amount of time possible.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this talk I will describe three projects that harness the potential of variable-aperture photography \u2013 capturing multiple photos by manipulating basic lens controls such as aperture and focus. I will show that by combining such photos, the information encoded in defocus can be used to achieve a variety of goals. First, I will describe [&hellip;]<\/p>\n","protected":false},"featured_media":194769,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-182835","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/MGB2BEKZg9w","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/182835","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/182835\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/194769"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=182835"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=182835"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=182835"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=182835"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=182835"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=182835"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=182835"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=182835"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=182835"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=182835"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}