{"id":159818,"date":"2010-07-01T00:00:00","date_gmt":"2010-07-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/image-deblurring-using-inertial-measurement-sensors\/"},"modified":"2020-09-25T17:48:14","modified_gmt":"2020-09-26T00:48:14","slug":"image-deblurring-using-inertial-measurement-sensors","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/image-deblurring-using-inertial-measurement-sensors\/","title":{"rendered":"Image Deblurring using Inertial Measurement Sensors"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present a deblurring algorithm that uses a hardware attachment coupled with a natural image prior to deblur images from consumer cameras. Our approach uses a combination of inexpensive gyroscopes and accelerometers in an energy optimization framework to estimate a blur function from the camera\u2019s acceleration and angular velocity during an exposure. We solve for the camera motion at a high sampling rate during an exposure and infer the latent image using a joint optimization. Our method is completely automatic, handles per-pixel, spatially-varying blur, and out-performs the current leading image-based methods. Our experiments show that it handles large kernels \u2013 up to at least 100 pixels, with a typical size of 30 pixels. We also present a method to perform \u201cground-truth\u201d measurements of camera motion blur. We use this method to validate our hardware and deconvolution approach. To the best of our knowledge, this is the first work that uses 6 DOF inertial sensors for dense, per-pixel spatially-varying image deblurring and the first work to gather dense ground-truth measurements for camera-shake blur.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a deblurring algorithm that uses a hardware attachment coupled with a natural image prior to deblur images from consumer cameras. Our approach uses a combination of inexpensive gyroscopes and accelerometers in an energy optimization framework to estimate a blur function from the camera\u2019s acceleration and angular velocity during an exposure. We solve for 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