DOI 10.1515/jisys-2013-0033 Journal of Intelligent Systems 2013; 22(3): 299–315 Research Article Rajkumar L. Biradar* and Vinayadatt V. Kohir Texture Inpainting Using Covariance in Wavelet Domain Abstract: In this article, the covariance of wavelet transform coefficients is used to obtain a texture inpainted image. The covariance is obtained by the maximum likelihood estimate. The integration of wavelet decomposition and maximum likelihood estimate of a group of pixels (texture) captures the best-fitting texture used to fill in the inpainting area. The image is decomposed into four wavelet coefficient images by using wavelet transform. These wavelet coefficient images are divided into small square patches. The damaged region in each coefficient image is filled by searching a similar square patch around the damaged region in that particular wavelet coefficient image by using covariance. Keywords: Inpainting, texture, maximum likelihood estimate, covariance, wavelet transform. *Corresponding author: Rajkumar L. Biradar, G. Narayanamma Institute of Technology and Science, Electronics and Tel ETM Department, Shaikpet, Hyderabad 500008, India, e-mail:
[email protected] Vinayadatt V. Kohir: Poojya Doddappa Appa College of Engineering, Gulbarga 585102, India 1 Introduction Image inpainting is the technique of filling in damaged regions in a non-detect- able way for an observer who does not know the original damaged image. The concept of digital inpainting was introduced by Bertalmio et al. [3]. In the most conventional form, the user selects an area for inpainting and the algorithm auto- matically fills in the region with information surrounding it without loss of any perceptual quality. Inpainting techniques are broadly categorized as structure inpainting and texture inpainting.