A recursive algorithm for maximum likelihood-based identification of blur from multiple observations

A maximum likelihood-based method is proposed for blur identification from multiple observations of a scene. When the relations among the blurring functions are known, the estimate of blur obtained using the proposed method is very good. Since direct computation of the likelihood function becomes difficult as the number of images increases, we propose an algorithm to compute the likelihood function recursively.

[1]  Florin Popentiu,et al.  Iterative identification and restoration of images , 1993, Comput. Graph..

[2]  A. Murat Tekalp,et al.  Identification of image and blur parameters for the restoration of noncausal blurs , 1986, IEEE Trans. Acoust. Speech Signal Process..

[3]  Subhasis Chaudhuri,et al.  Maximum likelihood estimation of blur from multiple observations , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Herbert Freeman,et al.  Machine Vision for Inspection and Measurement , 1989 .

[5]  Reginald L. Lagendijk,et al.  Hierarchical blur identification , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[6]  Aggelos K. Katsaggelos,et al.  Iterative deconvolution using several different distorted versions of an unknown signal , 1983, ICASSP.

[7]  Aggelos K. Katsaggelos,et al.  Image identification and restoration based on the expectation-maximization algorithm , 1990 .

[8]  Reginald L. Lagendijk,et al.  Identification and restoration of noisy blurred images using the expectation-maximization algorithm , 1990, IEEE Trans. Acoust. Speech Signal Process..

[9]  Muralidhara Subbarao Efficient Depth Recovery through Inverse Optics , 1989 .

[10]  Rabab Kreidieh Ward Restoration of differently blurred versions of an image with measurement errors in the PSF's , 1993, IEEE Trans. Image Process..

[11]  John W. Woods,et al.  Image identification and restoration in the subband domain , 1994, IEEE Trans. Image Process..

[12]  A. Murat Tekalp,et al.  Maximum likelihood image and blur identification: a unifying , 1990 .

[13]  Subhasis Chaudhuri,et al.  Optimal selection of camera parameters for recovery of depth from defocused images , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Subhasis Chaudhuri,et al.  Space-Variant Approaches to Recovery of Depth from Defocused Images , 1997, Comput. Vis. Image Underst..

[15]  Dennis C. Ghiglia Space-invariant deblurring given N independently blurred images of a common object , 1984 .

[16]  F. Graybill An introduction to linear statistical models , 1961 .