..............................
            ..............................
            ..............................
            
Application of Framelet Transform and Singular Value Decomposition to Image Enhancement
        
        In  this  paper,  a  new  satellite  image  enhancement  technique  based  on framelet  transform  and Singular Value 
Decomposition (SVD) has been proposed. Framelet transform is used to decompose the image into one low frequency subband 
and  eight  high  frequency  subbands.  The enhancement  is  done  with  regard  of  both  resolution  and  contrast.  To  increase  the 
resolution,  low  and  high  frequency  subbands  have  been  interpolated.  In  intermediate  stage,  estimating  high  frequency 
subbands  has  been  proposed  to  achieve  sharpness.  All  the  subbands  are  combined  by  inverse framelet  transform  to  get  the 
high  resolution  image.  To  increase  the  contrast, framelet  transform  is  combined  with SVD. Singular  values  of  the  low 
frequency  subband are updated  and inverse  transform  is  performed  to  get  the  enhanced  image. The  proposed  technique  has 
been  tested  on  satellite  images.  The  quantitative  measures  such  as Peak Signal-to-Noise Ratio (PSNR),  Structural Similarity 
Index Measure (SSIM), Universal Quality Index (UQI),  Entropy,  Quality_ Score are  used and the visual  results  show  the 
superiority  of  the  proposed  technique  over  the  conventional  and  state-of-art  image  enhancement  techniques.  The  time 
complexity indicates the proposed image enhancement is suitable for further image processing applications.    
            [1] Abdelnour A. and Selesnick I., Symmetric Nearly Shift-Invariant Tight Frame Wavelets, IEEE Transactions on Signal Processing, vol. 53, no. 1, pp. 231-39, 2005.
[2] Bhushan D., Sowmya V., and Soman K., Super Resolution Blind Reconstruction of Low Resolution Images Using Framelets Based Fusion, in proceedings of International Conference on Recent Trends in Information, Telecommunication and Computing, Kerala, pp.100-104, 2010. 648 The International Arab Journal of Information Technology, Vol. 15, No. 4, July 2018
[3] Demirel H. and Anbarjafari G., Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement, IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 6, pp. 1997-2004, 2010.
[4] Demirel H. and Anbarjafari G., Image Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition, IEEE Transactions on Geoscience and Remote Sensing, vol. 20, no. 5, pp. 1458-1460, 2011.
[5] Demirel H., Ozcinar C., and Anbarjafari G., Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition, IEEE Geoscience and Remote Sensing Letters, vol. 7, no. 2, pp. 333-337, 2010.
[6] Hadeel N. and Taai A., A Novel Fast Computing Method for Framelet Coefficients, American Journal of Applied Sciences, vol. 5, no. 11, pp.1522-1527, 2008.
[7] Harish G. and Singh G., Quality Assessment of Fused Image of MODIS and PALSAR, Progress in Electromagnetics Research, vol. 24, no. 24, pp. 191-221, 2010.
[8] Hong R., Li S., and Wu X., A Novel Similarity Based Quality Metric for Image Fusion, in Proceedings of International Conference on Audio, Language and Image Processing, Shanghai, pp. 167-172, 2008.
[9] Ibrahim H. and Kong N., Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement, IEEE Transactions on Consumer Electronics, vol. 53, no. 4, pp.1752- 58, 2007.
[10] Lal S. and Chandra M., Efficient Algorithm for Contrast Enhancement of Natural Images, The International Arab Journal of Information Technology, vol. 11, no.11, pp. 95-102, 2014.
[11] Mohsin M., No Reference Image Quality Assessment Depending on YCbCr and L*u*v, European Scientific Journal, vol. 3, pp. 119-131, 2013.
[12] Soman K., Resmi N., and Ramachandran K., Insight In to Wavelets: from Theory to Practice, PHI Learning, 2010.
[13] Starck J., Murtagh F., Cand s E., and Donoho D., Gray and Color Image Contrast Enhancement by the Curvelet Transform, IEEE Transactions on Image Processing, vol. 12, no. 6, pp. 706-717, 2003.
[14] Sulochana S. and Vidhya R., High Resolution Image Fusion based on Feature Motivated Pulse Coupled Neural Networks (PCNN) in Framelet Transform Domain, International Journal of earth sciences and Engineering, vol. 6, no. 2, pp. 291-296, 2013.
[15] Venkata R., Sudhakar N., Ravindra B., and Pratap L., An Image Quality Assessment Technique Based on Visual Regions of Interest Weighted Structural Similarity, GVIP Journal, vol. 6, no. 2, 2006.
[16] Wang Z. and Bovik A., A Universal Image Quality Index, IEEE Signal Processsing Letters, vol. 9, no. 3, pp. 81-84, 2002.
[17] Wang Z., Sheikh H., and Bovik A., No- Referenc Perceptual Quality Assessment of JPEG Compressed Images, in proceedings of International Conference on Image Processing, New York, pp. 1477-11480, 2002.
[18] Yeong-Taekgi K., Contrast Enhancement Using Brightness Preserving Bi-Histogram Equalization, IEEE Transactions on Consumer Electronics, vol. 43, no. 1, pp. 1-8, 1997.
[19] Yoon B. and Song W., Image Contrast Enhancement Based on the Generalized Histogram, Journal of Electronic Imaging, vol. 16, no. 3, 2007.
[20] Zuo C., Chen Q., Sui X., and Ren J., Brightness Preserving Image Contrast Enhancement using Spatially Weighted Histogram Equalization, The International Arab Journal of Information Technology, vol. 11, no. 1, pp. 25-32, 2014. Application of Framelet Transform and Singular Value Decomposition ... 649 Vidhya Rangasamy is an Associate professor at the Institute of Remote Sensing, Anna University, Chennai. She completed her B.E in Civil Engineering with Honors, M. Tech and Ph.D in Remote Sensing. She has 25 years of experience in teaching and Research. Her field of interest is in Remote Sensing data analysis, application to water resources. Sulochana Subramaniam is a Research scholar at Institute of Remote Sensing (IRS), Anna University, Chennai, India. She has completed M.Sc. physics and M. Tech in Remote Sensing and Wireless Sensor Networks. Her research interest includes image processing using wavelets, remote sensing, GIS and machine learning concepts. Vijayasekaran Duraisamy is a Research fellow at Institute of Remote sensing Anna University; he completed his Bachelor s degree in Botany and M.Sc in Remote sensing. He has 5 years of experience in research projects related to remote sensing and GIS. His field of interest includes remote sensing invasive ecology, Socio economic impacts of alien plants. Mohanraj Karuppanan is a Technical lead in Wipro Technologies, Bangalore, Karnataka, India. He completed B.E in Electrical Engineering and M.E in Computer Science. His research interest includes image processing, computer networks and telecom management solutions.