Image magnification by least squares surfaces

Document Type : Research Article

Authors

Yazd University

Abstract

Image magnification is one of the current issues of image processing in which keeping the quality and structure of images is the main concern. In im- age magnification, it is necessary to insert information in extra pixels. Adding information to an image should be compatible with the image structure with- out making artificial blocks. In this research, extra pixels are estimated using the surface of least squares, and all the pixels are reviewed according to the suggested edge-improving algorithm. The suggested ethod keeps the edges and minimizes the magnified image opacity and the artificial blocks. Numer- ical results are presented by using PSNR and SSIM fidelity measures and compared to some other methods. The average PSNR of the original image and image zooming is 32.79 which it shows that image zooming is very similar to the original image. Experimental results show that the proposed method has a better performance than others and provides good image quality.

Keywords


1] Allebach, J. and Wong, P. W. Edge-directed interpolation, in Proc. IEEE Int. Conf. Image. Proc., 3 (1996), 707-710.
[2] Battiato, S., Gallo, G. and Stanco, F. A locally adaptive zooming algorithm for digital images, Elsevier Science. Image and Vision Computing Journal, 20 (2002), 805-812.
[3] Carrato, S. and Tenze, L. A high quality 2X image interpolator, IEEE Signal Process., 7 (2000),132-135.
[4] Cha, Y. and Kim, S. The error-amended sharp edge (ease) scheme for image magnification, IEEE Trans. Image Process., 16 (2007), 1496-1505.
[5] Gonzalez, R.C. and Woods, R.E., Digital image processing, Prentice Hall, 2008.
[6] Hou, H. S. and Andrews, H. C. Cubic splines for image interpolation and digital filtering, IEEE Trans. Acoust., Speech, Signal Process., 26 (1978), 508-517.
[7] Jensen, K. and Anastassion, D. Subpixel edge localization and the interpolation of still images, IEEE Trans. Image Process., 4 (1995), 285-295
[8] Keys, R. G. Cubic convolution interpolation for digital image processing, IEEE Trans. Acoust., Speech, Signal Process., 29 (1981), 1153-1160.
[9] Kim, H., Cha, Y. and Kim, S. Curvature interpolation method for image zooming, IEEE Trans. Image Process., 20 (2011), 1895-1903.
[10] Lee, Y.J. and Yoon, J., Image zooming method using edge-directed moving least squares interpolation based on exponential polynomials, Applied Mathematics and Computation, 269 (2015), 569-583.
[11] Lee, Y. J. and Yoon, J. Nonlinear image up-sampling method based on radial basis function interpolation, IEEE Trans. Image Process., 19 (2010), 2682-2692.
[12] Lehmann, T., Gnner, C. and Spitzer, K. Survey: Interpolation methods in medical image processing, IEEE Trans. Med. Imaging., 18 (1999), 1049-1075.
[13] Li, M. and Nguyen, T. Q. Markov random field model-based edge directed image interpolation, IEEE Trans. Image Process., 17 (2008), 1121-1128.
[14] Li, X. and Orchard, T. New edge directed interpolation, IEEE Trans .Image Process.,10 (2001), 1521-1527.
[15] Online. Available:www.freeimages.co.uk and sipi.usc.edu/database.
[16] Padmanabhan, S. A. and Chandramathi, S. Image magnification using segmented polynomial interpolation in space, European Journal of Scientific Research, 57 (2011), 447-453.
[17] Thvenaz, P. , Blu, T. and Unser, M. Interpolation revisited, IEEE Trans. Med. Image., 19 (2000), 739-758.
[18] Wang, Z. , Bovik, A. C., Sheikh H. R. and Simoncelli E. P. , Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., 13 (2004), 600-612.
[19] Wang, Z. et al. The SSIM index for image quality assessment. MAT-LAB implementation available online from: http://www. cns. nyu. edu/lcv/ssim, (2003), 23-66.
[20] Youssef, D. Mohammed, B. Abdelmalek, A. Tarik, H. and El Miloud, J. Zoom and Restoring of Digital Images with Artificial Neural Networks, Computer Science and Engineering, 5 (2015),14-24.
[21] Zhang, L. and Wu, X. An edge-guided image interpolation algorithm via directional filtering and data fusion, IEEE Trans. Image Process., 15 (2006), 2226-2238.
[22] Zhang X. and Wu, X. Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation, IEEE Trans. Image Process., 17(2008), 887-896.