Image modeling and parameter identification for image restoration using a Kalman filter

Abstract

This paper proposes a Kalman filter based method for high accuracy image restoration. When a Kalman filter is applied to image restoration, the model of the original image affects the accuracy of the restoration. An effective model for restoration depends on the characteristics of the image or the condition of the observed image. On the other hand, the correlation of the original image and the variance of the noise are necessary for image restoration with a Kalman filter. If these parameters are unknown, they must be identified from an observed image which has been contaminated with additive noise. To address the above problems, a method is proposed that identifies the number of pixels used for estimation and their positions. A method to estimate the unknown parameters in the image restoration process is also proposed. In this paper, the performance of the proposed algorithm is verified by simulations. © 1998 Scripta Technica. Syst Comp Jpn, 29(3): 1–9, 1998

A Digital Track Map-Assisted SINS/OD Fusion Algorithm for Onboard Train Localization

“…In addition to being able to improve the positional accuracy of train positioning, the use of map-matching calculations can also correct errors such as gyro drift in inertial navigation systems and scale factors in odometers. Mapmatching algorithms exhibit a spectrum ranging from elementary search techniques to those using more complex mathematical techniques [18], including Kalman Filters [19] and Hidden Markov Models (HMMs) [20]. A number of different algorithms have been proposed for map matching across various application domains, each of which carries its own set of merits and demerits [21].…”