Title :
Image estimation in film-grain noise
Author :
Sadhar, S. Ibrahim ; Rajagopalan, A.N.
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Technol. Madras, Chennai, India
fDate :
3/1/2005 12:00:00 AM
Abstract :
A method based on the particle filter for recovering images degraded by film-grain noise is proposed. Due to the nonlinear relationship between the silver density and exposure, film-grain noise manifests itself as multiplicative non-Gaussian noise in the exposure domain. Since the posterior density is non-Gaussian, the proposed method works by representing it by a set of samples with associated weights. These samples are propagated in a recursive framework to obtain an optimal estimate of the original image. The effectiveness of the method is demonstrated with examples.
Keywords :
autoregressive processes; belief networks; image denoising; photographic emulsions; recursive filters; autoregressive process; film-grain noise; image estimation; image recovery; multiplicative nonGaussian noise; particle filter; recursive Bayesian framework; sensor nonlinearity; Bayesian methods; Degradation; Helium; Image sensors; Monte Carlo methods; Nonlinear filters; Particle filters; Recursive estimation; Silver; Yield estimation; Auto-regressive process; film-grain noise; image estimation; multiplicative noise; non-Gaussian noise; particle filter; recursive Bayesian framework; sensor nonlinearity;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2004.840850
