Image Deblurring using Inertial Measurement Sensors


Neel Joshi      Sing Bing Kang      C. Lawrence Zitnick      Richard Szeliski

Microsoft Research

An SLR Camera instrumented with our image deblurring attachment that uses inertial measurement sensors and the input image in an “aided blind-deconvolution” algorithm to automatically deblur images with spatially-varying blurs (first two images). A blurry input image (third image) and the result of our method (fourth image).


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’s 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 – up to at least 100 pixels, with a typical size of 30 pixels. We also present a method to perform “ground-truth” 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.


Automatically Deblurred using data from the Sensor Attachment (images are blinking between the blurred image and our deblurred result)


(2.96 MB)

(23.0 MB)


Ground-truth PSF Measurements

Spatially varying vs. spatially invariant PSFs

Copyright Microsoft Corporation 2010