Abstract

The identification of the point spread function (PSF) from the degraded image data constitutes an important first step in image restoration that is known as blur identification. Though a number of blur identification algorithms have been developed in recent years, two of the earlier methods based on the power spectrum and power cepstrum remain popular, because they are easy to implement and have proved to be effective in practical situations. Both methods are limited to PSF's which exhibit spectral nulls, such as due to defocused lens and linear motion blur. Another limitation of these methods is the degradation of their performance in the presence of observation noise. The central slice of the power bispectrum has been employed as an alternative to the power spectrum which can suppress the effects of additive Gaussian noise. In this paper, we utilize the bicepstrum for the identification of linear motion and defocus blurs. We present simulation results where the performance of the blur identification methods based on the spectrum, the cepstrum, the bispectrum and the bicepstrum is compared for different blur sizes and signal-to-noise ratio levels.

Publication Date

1994

Comments

"Blur identification based on higher order spectral nulls," Proceedings of the SPIE Conference on Image Reconstruction and Restoration, SPIE Volume 2302. The International Society for Optical Engineering. Held in San Diego, California: July 1994. Copyright 1994 Society of Photo-Optical Instrumentation Engineers. This paper is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. This work was supported in part by the NSF IUCRC Center for Electronic Imaging Systems. ISBN:0-8194-1626-6Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Article

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Campus

RIT – Main Campus

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