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Two-dimensional blind deconvolution.
| Content Provider | CiteSeerX |
|---|---|
| Author | Liang, Ben Pillai, S. Unnikrishna |
| Abstract | In this paper we examine the applicability of the previously proposedGreatest Common Divisor (GCD) method to blind image deconvolution. In this method, the desired image is approximated as the GCD of the two-dimensional polynomials corresponding to the ztransforms of two or more distorted and noisy versions of the same scene, assuming that the distortion filters areFIRandrelatively co-prime. We justify the breakdown of two-dimensional GCD into one-dimensional Sylvester-type GCD algorithms, which lowers the computational complexity while maintaining the noise robustness. A way of determining the supportsizeofthe true image is also described. We also provide a solution to deblurring using the GCD method when only one blurred image is available. Experimental results are shown using both synthetically blurred images and real motion-blurred pictures. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Two-dimensional Blind Deconvolution Computational Complexity Proposedgreatest Common Divisor Desired Image Image Deconvolution Two-dimensional Gcd Supportsizeofthe True Image Noisy Version One-dimensional Sylvester-type Gcd Algorithm Gcd Method Distortion Filter Two-dimensional Polynomial Noise Robustness Experimental Result Real Motion-blurred Picture |
| Content Type | Text |
| Resource Type | Article |
