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Publication Type : Conference Paper
Publisher : WCE
Source : Proceedings of the World Congress on Engineering 2011, WCE 2011, Volume 2, London, p.1563-1566 (2011)
Keywords : Algorithms, Computer vision, data base, Data fittings, DCT, Feature extraction, GLCM, Gray level co-occurrence matrix, High resolution, High resolution image, ILL-posed inverse problem, Image matching, Image processing and computer vision, Image segmentation, Image statistical features, Image super resolutions, Input image, Inverse problems, Low resolution, Low resolution images, MOD, OMP, Optimal direction, Single images, Sparse images, Sparse representation, Superresolution methods, Training image
Campus : Bengaluru
School : School of Engineering
Department : Electronics and Communication
Year : 2011
Abstract : 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.
Cite this Research Publication : 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.