Analysis of the practical coverage of uniform motions to approximate real camera shakes

Motion blur is usually modeled as the convolution of a latent image with a motion blur kernel, and most of current deblurring methods limit types of motion blurs to be uniform with the convolution model. However, real motion blurs are often non-uniform, and in consequence the methods may not well remove real motion blurs caused by camera shakes. To utilize the existing methods in practice, it is necessary to understand how much the uniform motions (i.e., translations) can approximate real camera shakes. In this paper, we analyze the displacement of real camera motions on image pixels and present the practical coverage of uniform motions (i.e., translations) to approximate complicated real camera shakes. We first analyze mathematically the difference of the motion displacement between the optical axis and image boundary under real camera shakes, then derive the practical coverage of uniform motion deblurring methods when used for real blurred images. The coverage can effectively guide how much one can utilize the existing uniform motion deblurring methods, and informs the need to model real camera shakes accurately rather than assuming uniform motions.

[1]  Sundaresh Ram,et al.  Removing Camera Shake from a Single Photograph , 2009 .

[2]  Michael S. Brown,et al.  Richardson-Lucy Deblurring for Scenes under a Projective Motion Path , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  S. B. Kang,et al.  Image deblurring using inertial measurement sensors , 2010, SIGGRAPH 2010.

[4]  Jiaya Jia,et al.  High-quality motion deblurring from a single image , 2008, ACM Trans. Graph..

[5]  Li Xu,et al.  Two-Phase Kernel Estimation for Robust Motion Deblurring , 2010, ECCV.

[6]  Seungyong Lee,et al.  Fast motion deblurring , 2009, ACM Trans. Graph..