Title :
Unscented Kalman Filter for Image Estimation in Film-Grain Noise
Author :
Subrahmanyam, G.R.K.S. ; Rajagopalan, A.N. ; Aravind, R.
Author_Institution :
Indian Inst. of Technol., Chennai
fDate :
Sept. 16 2007-Oct. 19 2007
Abstract :
This paper presents a novel approach based on the unscented Kalman filter (UKF) for image estimation in film-grain noise. The image prior is modeled as non-Gaussian and is incorporated within the UKF frame work using importance sampling. A small carefully chosen deterministic set of sigma points is used to capture the prior and is propagated through film-grain nonlinearity to compute image statistics. Experimental results are given to demonstrate the efficacy of the proposed method.
Keywords :
Kalman filters; image denoising; image restoration; importance sampling; film-grain noise; image estimation; image statistics; nonGaussian model; sigma points set; unscented Kalman filter; Additive white noise; Degradation; Image restoration; Markov random fields; Monte Carlo methods; Motion pictures; Random variables; Statistics; Taylor series; Wiener filter; Film-grain noise; Importance sampling; Markov random fields; Unscented Kalman filter;
Conference_Titel :
Image Processing, 2007. ICIP 2007. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-1437-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2007.4379942
