{"id":159181,"date":"2010-03-01T00:00:00","date_gmt":"2010-03-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/removing-rolling-shutter-wobble\/"},"modified":"2018-10-16T20:01:56","modified_gmt":"2018-10-17T03:01:56","slug":"removing-rolling-shutter-wobble","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/removing-rolling-shutter-wobble\/","title":{"rendered":"Removing Rolling Shutter Wobble"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present an algorithm to remove wobble artifacts from a video captured with a rolling shutter camera undergoing large accelerations or jitter. We show how estimating the rapid motion of the camera can be posed as a temporal super-resolution problem. The low-frequency measurements are the motions of pixels from one frame to the next. These measurements are modeled as temporal integrals of the underlying high-frequency jitter of the camera. The high-frequency estimated motion of the camera is then used to re-render the sequence as though all the pixels in each frame were imaged at the same time. We also present an auto-calibration algorithm that can estimate the time between the capture of subsequent rows in the camera.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present an algorithm to remove wobble artifacts from a video captured with a rolling shutter camera undergoing large accelerations or jitter. We show how estimating the rapid motion of the camera can be posed as a temporal super-resolution problem. The low-frequency measurements are the motions of pixels from one frame to the next. 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