Blur identification from vector quantizer encoder distortion

Author

Panchapakesan, Kannan ; Sheppard, David G. ; Marcellin, Michael W. ; Hunt, Bobby R.

Author_Institution

Dept. of Electr. & Comput. Eng., Arizona Univ., Tucson, AZ, USA

Volume

10

Issue

3

fYear

2001

fDate

3/1/2001 12:00:00 AM

Firstpage

465

Lastpage

470

Abstract

Blur identification is a crucial first step in many image restoration techniques. An approach for identifying image blur using vector quantizer encoder distortion is proposed. The blur in an image is identified by choosing from a finite set of candidate blur functions. The method requires a set of training images produced by each of the blur candidates. Each of these sets is used to train a vector quantizer codebook. Given an image degraded by unknown blur, it is first encoded with each of these codebooks. The blur in the image is then estimated by choosing from among the candidates, the one corresponding to the codebook that provides the lowest encoder distortion. Simulations are performed at various bit rates and with different levels of noise. Results show that the method performs well even at a signal-to-noise ratio (SNR) as low as 10 dB

Keywords

image coding; image restoration; vector quantisation; SNR; VQ; candidate blur functions; codebook training; image blur identification; image restoration; signal-to-noise ratio; training images; vector quantizer encoder distortion; Circuit noise; Degradation; Image restoration; Noise level; Optical distortion; Optical films; Optical noise; Optical recording; Optical signal processing; Signal to noise ratio;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.908524

Filename

908524