Correction of Spatially Varying Image and Video Motion Blur Using a Hybrid Camera

  • Authors:

  • Yu-Wing Tai;Hao Du;Michael S. Brown;Stephen Lin

  • Affiliations:

  • Korea Advanced Institute of Science and Technology (KAIST), Korea;University of Washington, Seattle;National University of Singapore, Singapore;Microsoft Research Asia, Beijing

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 2010

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Abstract

We describe a novel approach to reduce spatially varying motion blur in video and images using a hybrid camera system. A hybrid camera is a standard video camera that is coupled with an auxiliary low-resolution camera sharing the same optical path but capturing at a significantly higher frame rate. The auxiliary video is temporally sharper but at a lower resolution, while the lower frame-rate video has higher spatial resolution but is susceptible to motion blur. Our deblurring approach uses the data from these two video streams to reduce spatially varying motion blur in the high-resolution camera with a technique that combines both deconvolution and super-resolution. Our algorithm also incorporates a refinement of the spatially varying blur kernels to further improve results. Our approach can reduce motion blur from the high-resolution video as well as estimate new high-resolution frames at a higher frame rate. Experimental results on a variety of inputs demonstrate notable improvement over current state-of-the-art methods in image/video deblurring.