Sparse representation of images finds many applications in image processing and computer vision. Recently various attempts have been made to regularize the ill-posed inverse problem of motion free image super resolution using sparse representation of low resolution image patches. However the proposed method in this paper is different from the previous approaches reported in the literature in terms of method of dictionary training and feature extraction from the trained data base images. Gray Level Co-Occurrence Matrix(GLCM) is a proven method for extracting image statistical features, which are used mainly for image classification, segmentation etc. In the present work we have extracted GLCM parameters for regularization of the data fitting term of the cost function of the image super resolution model. We used Matching Optimal Directions (MOD) algorithm for obtaining high resolution and low resolution dictionaries from training image patches and seek the sparse representation of low resolution input image patch using low resolution dictionary and then obtain high resolution image patch from high resolution dictionary. The results of the proposed algorithm showed visual, PSNR, RMSE, and SSIM improvements over other super resolution methods.
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@2d8b77c7 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@1f64c12d Through org.apache.xalan.xsltc.dom.DOMAdapter@fc68175; Conference Code:87249
Sa Ravishankar, Reddy, N. Ca, and Joshi, M. Vb, “Single image super resolution using sparse image and GLCM statistics as priors”, in Proceedings of the World Congress on Engineering 2011, WCE 2011, London, 2011, vol. 2, pp. 1563-1566.