Recovering affine motion and defocus blur simultaneously

There are at least two situations in practical computer vision where displacement of a point in an image is accompanied by a defocus blur. The first is when a camera of limited autofocal capability moves in depth, and the second is when a limited autofocal camera zooms. Motion and zooming are two popular strategies for acquiring more detail or for acquiring depth. The defocus blur has been considered noise or at best been ignored. However, the defocus blur is in itself a cue to depth, and hence we proceed to show how it can be calculated simultaneously with affine motion. We first introduce the theory, then develop a solution method and finally demonstrate the validity of the theory and the solution by conducting experiments with real scenery.

[1]  Michel Dhome,et al.  Three-dimensional reconstruction by zooming , 1993, IEEE Trans. Robotics Autom..

[2]  Steven A. Shafer,et al.  Depth from focusing and defocusing , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Jun Ma,et al.  Depth from zooming , 1990 .

[4]  J. Koenderink,et al.  Extraction of motion parallax structure in the visual system I , 2004, Biological Cybernetics.

[5]  Peter Lawrence,et al.  A matrix based method for determining depth from focus , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Michal Irani,et al.  Recovery of ego-motion using image stabilization , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  R. Manmatha,et al.  A framework for recovering affine transforms using points, lines or image brightnesses , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Alex Pentland,et al.  A New Sense for Depth of Field , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Shree K. Nayar,et al.  Real-time focus range sensor , 1995, Proceedings of IEEE International Conference on Computer Vision.

[10]  Rama Chellappa,et al.  A generalized motion model for estimating optical flow using 3-D Hermite polynomials , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[11]  Bijan G. Mobasseri,et al.  Virtual motion: 3-D scene recovery using focal length-induced optic flow , 1994, Proceedings of 1st International Conference on Image Processing.

[12]  Murali Subbarao,et al.  Focused image recovery from two defocused images recorded with different camera settings , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Berthold K. P. Horn Robot vision , 1986, MIT electrical engineering and computer science series.

[14]  Murali Subbarao,et al.  Depth from defocus by changing camera aperture: a spatial domain approach , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.