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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/36635

Title: Space-Varying Nonstationary Digital Image Model
Authors: Jia-Chyi Wu
Contributors: NTOU:Department of Communications Navigation and Control Engineering
Keywords: digital image model;space-varying autoregressive process;nonstationary process;quadtree segmentation;least-squares estimator
Date: 2013-01
Issue Date: 2015-05-19T06:37:36Z
Publisher: Information Journal
Abstract: Mathematical image models are important in the development of useful algorithms for digital image processing. The stationary image model leads to mathematical simplicity, but fails to describe the realistic and inherent nonstationary behavior of images. In order to accurately estimate the nonstationary statistics of a real-world image, we proposed a variable blocksize space-varying autoregressive (SVAR) nonstationary Gauss-Markov image model. The variable blocksize segmentation is performed using a quadtree data structure by dividing the perceptually more important regions of an image into smaller size blocks compared to the size of blocks containing lesser amounts of spatial activity. Each image block is described by an SVAR Gauss-Markov random field (GMRF), where the SVAR parameters are estimated using an on-line modified least-squares estimator. The proposed nonstationary image modeling procedure which can more accurately characterize the nonstationarity inherent in a real-world image is an improvement over the stationary modeling procedures employed in the past.
Relation: 16(1(A)), pp.175-189
URI: http://ntour.ntou.edu.tw/handle/987654321/36635
Appears in Collections:[通訊與導航工程學系] 期刊論文

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