Image Modeling Appropriate for Kalman Filtering

• Main Authors: Kuo-Wei Tai, 戴國瑋
• Other Authors: Ben-Shung Chow
• Format: Others
• Language: en_US
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/39710983656880027662

碩士 === 國立中山大學 === 電機工程學系研究所 === 88 === In stochastic representation an image is a sample function of an array of random variables which is called a random field. For characterizing an ensemble of images, we choose an autoregressive model as our image model. An image model often applies to image processing such as image data compression and image restoration. Therefore the validity of the image model affect it’s performance of image processing. The output of the AR model depends on its parameters – system transition matrix and generating noise. Hence the validity of this model is related to these two parameters. How to seek the standard of the validity of the image model is a problem. We exploit performance of image model’s application – image restoration - to find a method of determining the validity of the image model. In our paper we find a relation between image restoration performance and image model’s parameters by the Kalman filtering equations. An image model with lower generating noise power and system transition matrix is better for image restoration and is considered a good image model. In the analysis of the parameters of the image model, we can meet the requirements of the parameters by image segmentation method, residual image method and normalized image method. In addition it also helps us understand the Kalman filter much more and know how to find the solution of similar problems.