Abstract
Color models are used for facilitating the specification of colors in a standard way. A suitable color model is associated with every application based on color space. This paper mainly focuses on the analysis of effectiveness of different color models on single image scale-up problems. Single image scale-up aims in the recovery of original image, where the input image is a blurred and down- scaled version of the original one. In order to identify the effect of different color models on scale-up of single image applications, the experiment is performed with the single image scale-up algorithm on standard image database. The performance of different color models (YCbCr, YCoCg, HSV, YUV, CIE XYZ, Photo YCC, CMYK, YIQ, CIE Lab, YPbPr) are measured by quality metric called Peak Signal to Noise Ratio (PSNR). The experimental results based on the calculated PSNR values prove that YCbCr and CMYK color models give effective results in single image scale-up application when compared with the other available color models.
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References
Bruckstein, A.M., Donoho, D.L., Elad, M.: From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review 51(1), 34–81 (2009)
Candès, E.J., Fernandez-Granda, C.: Towards a mathematical theory of super-resolution. Communications on Pure and Applied Mathematics 67(6), 906–956 (2014)
Cristani, M., Cheng, D.S., Murino, V., Pannullo, D.: Distilling information with super-resolution for video surveillance. In: Proceedings of the ACM 2nd International Workshop on Video Surveillance & Sensor Networks, pp. 2–11. ACM (2004)
Elad, M.: Sparse and redundant representations: from theory to applications in signal and image processing. Springer (2010)
Elad, M., Aharon, M.: Image denoising via learned dictionaries and sparse representation. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 895–900. IEEE (2006)
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing 15(12), 3736–3745 (2006)
Karimi, S.J.E., Kangarloo, K.: A survey on super-resolution methods for image reconstruction. International Journal of Computer Applications 19(3), 0975–8887 (2014)
Farsiu, S., Robinson, D., Elad, M., Milanfar, P.: Advances and challenges in super-resolution. International Journal of Imaging Systems and Technology 14(2), 47–57 (2004)
Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Transactions on Image Processing 13(10), 1327–1344 (2004)
Fernandez-Granda, C., Candes, E.J.: Super-resolution via transform-invariant group-sparse regularization. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 3336–3343. IEEE (2013)
Ford, A., Roberts, A.: Colour space conversions, vol. 1–31. Westminster University, London (1998)
Hao, S., Lin, L., Weiping, Z., Limin, L.: Location and super-resolution enhancement of license plates based on video sequences. In: 2009 1st International Conference on Information Science and Engineering (ICISE), pp. 1319–1322. IEEE (2009)
Intel\( \textregistered \) integrated performance primitives for intel\(\textregistered \) architecture reference manual, vol. 2. Image and video processing
Keith, J.: Video Demystified. A Handbook for the Digital Engineer. Newnes (2004)
KratochvÃl, T., Melo, J.: Utilization of matlab for tv colorimetry and color spaces analysis
Kunter, M., Kim, J., Sikora, T.: Super-resolution mosaicing using embedded hybrid recursive folow-based segmentation. In: 2005 Fifth International Conference on Information, Communications and Signal Processing, pp. 1297–1301. IEEE (2005)
Li, F., Jia, X., Fraser, D.: Universal hmt based super resolution for remote sensing images. In: 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 333–336. IEEE (2008)
Maintz, J.B., Viergever, M.A.: A Viergever. A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)
Maji, S.: Generative Models for Super-Resolution Single Molecule Microscopy Images of Biological Structures. PhD thesis, National Institutes of Health (2009)
Richardson, I.E.G.: Video Codec Design. Willey Interscience (2002)
Roohi, S., Zamani, J., Noorhosseini, M., Rahmati, M.: Super-resolution mri images using compressive sensing. In: 2012 20th Iranian Conference on Electrical Engineering (ICEE), pp. 1618–1622. IEEE (2012)
Schultz, R.R., Stevenson, R.L.: Extraction of high-resolution frames from video sequences. IEEE Transactions on Image Processing 5(6), 996–1011 (1996)
Marcollin, M.W., Sheppard, D.G., Hunt, D.R.: Iterative multiframe super-resolution algorithms for atmospheric turbulance- degraded imagery. In: IEEE International Conference on Acoustic Speech and Signal Processing, vol. 5, pp. 2857–2860, May 12–15, 1998
Yu, H., Xiang, M., Hua, H., Chun, Q.: Face image super-resolution through pocs and residue compensation. Visual Information Engineering (2008)
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2011. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012)
Zhang, Y., Mishra, R.K.: A review and comparison of commercially available pan-sharpening techniques for high resolution satellite image fusion. In: 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 182–185. IEEE (2012)
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John, N., Viswanath, A., Sowmya, V., Soman, K.P. (2016). Analysis of Various Color Space Models on Effective Single Image Super Resolution. In: Berretti, S., Thampi, S., Srivastava, P. (eds) Intelligent Systems Technologies and Applications. Advances in Intelligent Systems and Computing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-319-23036-8_46
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