Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 158 countries worldwide.
Learning Based Single Frame Image Super-resolution Using Fast Discrete Curvelet Coefficients
Anil A. Patil, Jyoti Singhai
Pages - 283 - 296     |    Revised - 15-09-2012     |    Published - 24-10-2012
Volume - 6   Issue - 5    |    Publication Date - October 2012  Table of Contents
Super-resolution, Learning Method, Fast Discrete Curvelet Transform
High-resolution (HR) images play a vital role in all imaging applications as they offer more details. The images captured by the camera system are of degraded quality due to the imaging system and are low-resolution (LR) images. Image super-resolution (SR) is a process, where HR image is obtained from combining one or multiple LR images of same scene. In this paper, learning based single frame image super-resolution technique is proposed by using Fast Discrete Curvelet Transform (FDCT) coefficients. FDCT is an extension to Cartesian wavelets having anisotropic scaling with many directions and positions, which forms tight wedges. Such wedges allow FDCT to capture the smooth curves and fine edges at multiresolution level. The finer scale curvelet coefficients of LR image are learnt locally from a set of high-resolution training images. The super-resolved image is reconstructed by inverse Fast Discrete Curvelet Transform (IFDCT). This technique represents fine edges of reconstructed HR image by extrapolating the FDCT coefficients from the high-resolution training images. Experimentation based results show appropriate improvements in MSE and PSNR.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 R.Y.Tsai and T.S.Hung, ”Multiframe image restoration and registration,” in Advances in Computer Vision and Image Processing,vol.1,chapter 7,pp.317-339.JAI press,greenwich,Conn,USA,1984.
2 M.Irani and S. Peleg.” Improving resolution by image registration,” CVGIP: Graphical Models and Image Processing, vol. 53, no. 3 pp. 231-239, 1991.
3 M. Irani and S. Peleg,” Motion analysis for image enhancement: resolution, occlusion, and transparency,” Journal of Visual Communication and Image Representation, vol. 4, no. 4,pp. 324-335,1993.
4 M. K. Ng, J. Koo, and N. K. Bose.” Constraintet total least-sqeuare nsorcomputations for high-resolution image reconstruction with multisensors,” International Journal Of Imaging Systems and Technology, vol. 12, no. 1, pp. 35-42, 2002.
5 M. K. Ng and N. K. Bose,” Analysis of displacement errors in high-resolution image reconstruction with multisensors,” IEEE Transactions on Circuits and Systems Part I, vol.49, no. 6, pp. 806-813,2002.
6 N. Nguyen, P. Milanfar, and G. Golub,” A computationally efficient super-resolution image reconstruction algorithm,” IEEE transaction on Image Processing, vol. 10, no. 4, pp. 573-583, 2001
7 R.R. Schuitz and R. L. Stevenson,” A Bayesian approach to image expansion for improved definition,” IEEE Transactions on Image Processing, vol. 3, no. 3, pp. 233-242,1994
8 D. Rajan and S.Chaudhuri,” An MRF-based approach to generation of super-resolution images from blurred observations,” Journal of mathematical Imaging and Vision, vol. 16,no. 1, pp. 5-15, 2002.
9 D. Rajan and S. Chaudhuri,” Simultaneous estimation of super-resolved scene and depth map from low resolution defocused observations,” IEEE transctions on Pattern Analysis and Machine Intelligance, vol. 25,no. 9, pp. 1102-1117, 2003
10 M. Elad and A. Feuer,” Restoration of a single super-resolution image from several blurred,noisy and under sampled measured images,” IEEE transactions on Image Processing, vol.6. no.12. pp. 1646-1658, 1997
11 S. Baker and T. Kanade,” Limits on super-resolution and how to break them,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1167-1183,2002
12 D. Capel and A. Zisserman,” Super-resolution from multiple views using learnt image models,” In Proceedings of IEEE Computer Society Conference on Computer Vision and pattern Recognition (CVPR’01), vol. 2, pp. II-627-II-634, Kauai, Hawaii, USA, December 2001
13 W. T. Freeman, T. R. Jones, and E. C. pasztor,” Example-based super-resolution,” IEEE Computer Graphics and Applications, vol. 22, no. 2, pp. 56-65, 2002
14 M.V. Joshi and S. Chaudhuri,” Alearning-based method for image super-resolution from zoomed observations,” In Proceedings of 5th International Conference on Advances In Pattern Recognition (ICAPR’03), pp. 179-182, Culcutta, India, December 2003
15 C.V. Jiji, M. V. Joshi, and S. Chaudhuri,” Single –frame image super-resolution using learned wavelet coefficients,” International Journal of Imaging Systems and Technology,vol. 14, no. 3, pp. 105-112,2004
16 H. Chang, D. Y. Yeung, and Y. Xiong,” Super-resolution trough neighbor embedding,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’04), vol.1, pp. I-275-I-282, Washington, DC, USA, June-July 2004.
17 J. S. Park and S. W. Lee.” Enhancing low-resolution facial images using error backprojection for human identification at a distance,” in Proceedings of 17th IEEE International Conference on Pattern Recognition (ICPR’04), vol.1, pp. 346-349, Cambridge, UK, August 2004
18 J.Sun, N. N. Zheng, H. Tao, and H. Y. Shum,” image hallucination with primal sketch priors,”In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’03), vol. 2, pp. II-729-II-736, Madison, Wis, USA, June 2003.
19 M.N. Do and M. Vetterli,” The contourlet transform: an efficient directional multiresolution image representation,” IEEE transactions on Image Processing, vol. 14, no. 12, pp. 2091-2106, 2005
20 C.V.Jiji and S.Chaudhuri, ”Single-frame image Super-resolution through Contourlet Learning,”EURASIP Jounal on Applied Signal Processing ,vol.2006, Article ID 73767,pp.1-11,2006
21 D. Glasner, S. Bagon, and M. Irani,” Super-resolution from a single image,” IEEE International Conference on Computer Vision (ICCV), pp. 349-356,2009
22 P. P. Gajjar and M. V. Joshi,” New learning based super-resolution use of DWT and IGMRF prior,” IEEE Transactions on Image Processing, vol. 19, no. 5, pp. 1201-1213, May 2010.
23 C. Kim, K. Choi, H. Lee, K. Hwang, and J. B. Ra,” Robust Learning-Based Supreresolution,”in Proceedings of International Conference on Image Processing (ICIP’10), pp.2017-2020, Hong Kong, Sept. 2010.
24 K. I. Kim and Y. Kwon,” Sinlge-image Super-Resolution Using Spsrse regression and Natural Image prior,” IEEE Transcation on Pattern Analysis and Machine Intelligence, vol.32, no. 6, pp. 1127-1133, 2010.
25 E. J. Candes and D. L. Donoho,” New tight frames of curvelets and optimal representations of objects with piecewise-C2 singularities,” Comm. on Pure and Appl. Math. Vol.57, pp.219–266, 2004.
26 E.J.Candes, D.L.Donoho and L.Ying,” Fast Discrete Curvelet Transform.,” Journal of Multiscale modeling & simulation, vol.5, no.3, pp.861—899, 2006.
Mr. Anil A. Patil
- India
Mr. Jyoti Singhai
- India