Abstract
In recent times the spatial autoregressive models have been extensively used to represent images. In this paper we propose an algorithm to represent and reproduce texture images based on the estimation of spatial autoregressive processes. The image intensity is locally modeled by a first spatial autoregressive model with support in a strongly causal prediction region on the plane. A basic criteria to quantify similarity between two images is used to locally select this region among four different possibilities, corresponding to the four strongly causal regions on the plane. Two global image similarity measures are used to evaluate the performance of our proposal.
Highlights
The goal of this work is to introduce a new algorithm to represent and reproduce texture images that uses and improves other recent proposals concerning this topic.Most of the images of interest, for example, the images of cultivated fields and concentration of population are naturally rich in texture, level of gray, etc
To defined, a approximated image of is provided by the algorithm. This selection is based on the mean square error (MSE)
This paper proposes a new algorithm to represent and reproduce texture images based on the estimation of spatial autoregressive processes
Summary
The goal of this work is to introduce a new algorithm to represent and reproduce texture images that uses and improves other recent proposals concerning this topic.Most of the images of interest, for example, the images of cultivated fields and concentration of population are naturally rich in texture, level of gray, etc. Image representation and image texture recovery have been two important and challenging topics. In this sense the spatial autoregressive model (AR- 2D model) has been extensively used to represent images ([3], [14]) due to its two main properties. Theoretical properties of the first-order AR-2D model were studied by Basu and Reinsel ([2]). They derived the correlation structure of the model and the maximum likelihood estimators of the parameters. The spatial autoregressive models have benefited other topics in image processing like image segmentation. An approach to perform image segmentation based on the estimation of AR-2D processes has been recently suggested by Ojeda et al ([15])
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