title: Image recovery under nonlinear and non-Gaussian degradations.
abstract: A new two-dimensional recursive filter for recovering degraded images is proposed that is based on particle-filter theory. The main contribution of this work lies in evolving a framework that has the potential to recover images suffering from a general class of degradations such as system nonlinearity and non-Gaussian observation noise. Samples of the prior probability distribution of the original image are obtained by propagating the samples through an appropriate state model. Given the measurement model and the degraded image, the weights of the samples are computed. The samples and their corresponding weights are used to calculate the conditional mean that yields an estimate of the original image. The proposed method is validated by demonstrating its effectiveness in recovering images degraded by film-grain noise. Synthetic as well as real examples are considered for this purpose. Performance is also compared with that of an existing scheme.
authors.affiliation: Image Processing and Computer Vision Laboratory, Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India.
authors.forename: S I
authors.initials: SI
authors.lastname: Sadhar
authors.forename: A N
authors.initials: AN
authors.lastname: Rajagopalan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D010781:Photography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes