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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29414

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Better Perception of Low Resolution Images Using Wavelet Interpolation Techniques
High resolution images are always desired as they contain the more information and they can better represent the original data. So, to convert the low resolution image into high resolution interpolation is done. The quality of such high resolution image depends on the interpolation function and is assessed in terms of sharpness of image. This paper focuses on Wavelet based Interpolation Techniques in which an input image is divided into subbands. Each subband is processed separately and finally combined the processed subbandsto get the super resolution image. 
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[1] T. Gulati, M. Pal, “Interpreting Low Resolution CT Scan Images Using Interpolation Functions,” International Journal of Computer Applications, Vol 74– No.3, July 2013, pp. 50-57.
[2] T.Gulati, H.P. Sinha, “Interpreting Low Resolution MRI Images Using Polynomial Based Interpolation,” International Journal of Engineering Trends and Technology(IJETT) – Volume 10 Number 13 - Apr2014, pp. 626-631.
[3] A.Temizel, T.Vlachos,“Wavelet domain image resolution enhancement using cycle-spinning,” IET Journals &Magazines, Vol. 41, Issue 3,2005, pp. 119-121.
[4] Emil DUMIC, Sonja GRGIC, Mislav GRGIC, “The Use of Wavelets in Image Interpolation: Possibilities and Limitations,” Radio engineering, Vol. 16, No. 4, DECEMBER 2007, pp. 101-109.
[5] Numan Unaldi, Vijayan K. Asari, “Wavelet Transform based image interpolation,” Sixth international Symposium, ISVC 2010, Las Vegas, USA, November 29- December 1, 2010.
[6] Turgay Celik and Tardi Tjahjadi, “Image Resolution Enhancement Using Dual-tree Complex Wavelet Transform,” Geoscience and Remote Sensing Letters, Vol. 7(3), 2010,pp. 554 – 557.
[7] Gholamreza Anbarjafari and Hasan Demirel, “Image super resolution based on interpolation of wavelet domain high frequency subbands and the spatial domain input image,” ETRI J., Vol. 32, no. 3, Jun. 2010, pp. 390–394.
[8] Hasan Demirel and Gholamreza Anbarjafari,“Satellite image resolution enhancement using complex wavelet transform,” IEEE Geoscience Remote Sens. Lett., vol. 7, no. 1, Jan. 2010, pp. 123–126.
[9] Hasan Demirel and Gholamreza Anbarjafari, “Image resolution enhancement by using discrete and stationary wavelet decomposition,” IEEE T. Image Proc., 20(5), 2010b, pp. 1458-1460.
[10] Praksh P. Gajjar and Manjunath V. Joshi, “New Learning Based Super Resolution: Use of DWT and IGMRF Prior,” IEEE Transactions on Image Processing Vol. 19, No. 5, 2010.
[11] Hasan Demirel and Gholamreza Anbarjafari, “IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition,” IEEE Transactions on Image Processing, Vol. 20, No. 5, May 2011, pp. 1458-1460.
[12] K. Karthikeyan, C. Chandrasekar, “Wavelet-based Image Enhancement Techniques for Improving Visual Quality of Ultrasonic Images,” International Journal of Computer Applications (0975 – 8887) Volume 39– No.17, February 2012, pp. 49-53.
[13] Bagawade Ramdas P., Bhagawat Keshav S., Patil Pradeep M., “Wavelet Transform Techniques for Image Resolution Enhancement: A Study,” International Journal of Emerging Technology and Advanced Engineering Website: (ISSN 2250-2459, Volume 2, Issue 4, April 2012), pp. 167-172.
[14] Mayuri D Patil, Surbhi Khare, “Overview of techniques used for image resolution enhancement,” International Journal on Computer Science and Engineering (IJCSE), Vol. 4 No. 07 July 2012, pp. 1345-1347.
[15] S. Sangeetha, Y. Hari Krishna, “Image Resolution Enhancement Technique Based on the Interpolation of the High Frequency Subbands Obtained by DWT,” International Journal of Engineering Trends and Technology (IJETT) – Vol.4, Issue7, July 2013, pp. 3061-3067.
[16] P. Karunakar, V. Praveen and O. Ravi Kumar, “Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement,” Advance in Electronic and Electric Engineering, Volume 3, Number 4 (2013), pp. 405-412.
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