Two-dimensional blind deconvolution using a robust GCD approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we examine the applicability of the previously proposed Greatest 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 z-transforms of two or more distorted and noisy versions of the same scene, assuming that the distortion filters are FIR and relatively 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 support size of the 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.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
PublisherIEEE Comp Soc
Pages424-427
Number of pages4
Volume1
StatePublished - 1997
EventProceedings of the 1997 International Conference on Image Processing. Part 2 (of 3) - Santa Barbara, CA, USA
Duration: Oct 26 1997Oct 29 1997

Other

OtherProceedings of the 1997 International Conference on Image Processing. Part 2 (of 3)
CitySanta Barbara, CA, USA
Period10/26/9710/29/97

Fingerprint

Deconvolution
Motion pictures
FIR filters
Computational complexity
Polynomials
Mathematical transformations

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Liang, B., & Pillai, U. (1997). Two-dimensional blind deconvolution using a robust GCD approach. In IEEE International Conference on Image Processing (Vol. 1, pp. 424-427). IEEE Comp Soc.

Two-dimensional blind deconvolution using a robust GCD approach. / Liang, Ben; Pillai, Unnikrishna.

IEEE International Conference on Image Processing. Vol. 1 IEEE Comp Soc, 1997. p. 424-427.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liang, B & Pillai, U 1997, Two-dimensional blind deconvolution using a robust GCD approach. in IEEE International Conference on Image Processing. vol. 1, IEEE Comp Soc, pp. 424-427, Proceedings of the 1997 International Conference on Image Processing. Part 2 (of 3), Santa Barbara, CA, USA, 10/26/97.
Liang B, Pillai U. Two-dimensional blind deconvolution using a robust GCD approach. In IEEE International Conference on Image Processing. Vol. 1. IEEE Comp Soc. 1997. p. 424-427
Liang, Ben ; Pillai, Unnikrishna. / Two-dimensional blind deconvolution using a robust GCD approach. IEEE International Conference on Image Processing. Vol. 1 IEEE Comp Soc, 1997. pp. 424-427
@inproceedings{0daba858b0844ee2b142c2102a5d658b,
title = "Two-dimensional blind deconvolution using a robust GCD approach",
abstract = "In this paper we examine the applicability of the previously proposed Greatest 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 z-transforms of two or more distorted and noisy versions of the same scene, assuming that the distortion filters are FIR and relatively 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 support size of the 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.",
author = "Ben Liang and Unnikrishna Pillai",
year = "1997",
language = "English (US)",
volume = "1",
pages = "424--427",
booktitle = "IEEE International Conference on Image Processing",
publisher = "IEEE Comp Soc",

}

TY - GEN

T1 - Two-dimensional blind deconvolution using a robust GCD approach

AU - Liang, Ben

AU - Pillai, Unnikrishna

PY - 1997

Y1 - 1997

N2 - In this paper we examine the applicability of the previously proposed Greatest 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 z-transforms of two or more distorted and noisy versions of the same scene, assuming that the distortion filters are FIR and relatively 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 support size of the 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.

AB - In this paper we examine the applicability of the previously proposed Greatest 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 z-transforms of two or more distorted and noisy versions of the same scene, assuming that the distortion filters are FIR and relatively 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 support size of the 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.

UR - http://www.scopus.com/inward/record.url?scp=0031338950&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031338950&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0031338950

VL - 1

SP - 424

EP - 427

BT - IEEE International Conference on Image Processing

PB - IEEE Comp Soc

ER -