Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models

Authors

  • Ayoub Abderrazak Maarouf Laboratoire d’Automatique et de Robotique, Département d’Electronique Université des fréres Mentouri,Constantine 1 Algerie
  • Fella Hachouf Laboratoire d’Automatique et de Robotique, Département d’Electronique Université des fréres Mentouri,Constantine 1 Algerie
  • Soumia Kharfouchi Département de médecine, Bon Pasteur Chalet des Pins, Université Constantine 3 Algerie

DOI:

https://doi.org/10.5566/ias.2872

Keywords:

exemplar based method, GMM, local approximated images, texture synthesis, 2D-RCA models

Abstract

Example-based texture synthesis is a fundamental topic of many image analysis and computer vision applications. Consequently, its representation is one of the most critical and challenging topics in computer vision and pattern recognition, attracting much academic interest throughout the years. In this paper, a new statistical method to synthesize textures is proposed. It consists in using two indexed random coefficients autoregressive (2D-RCA) models to deal with this problem. These models have a good ability to well detect neighborhood information. Simulations have demonstrated that the 2D-RCA models are very suitable to represent textures. So, in this work, to generate textures from an example, each original image is splitted into blocks which are modeled by the 2D-RCA. The proposed algorithm produces approximations of the obtained blocks images from the original image using the generalized method of moments (GMM). Different sizes of windows have been used. This study offers some important insights into the newly generated image. Satisfying obtained results have been compared to those given by well-established methods. The proposed algorithm outperforms the state-of-the-art approaches.

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Published

2023-04-18

Issue

Section

Original Research Paper

How to Cite

Maarouf, A. A., Hachouf, F., & Kharfouchi, S. (2023). Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models. Image Analysis and Stereology, 42(1), 37-49. https://doi.org/10.5566/ias.2872