Abstract
Improvement of edge details in an image is basically a process of extracting high frequency details from the image and then adding this information to the blurred image. In this paper we propose an image sharpening technique in which high frequency details are extracted using wavelet transforms and then added with the blurred image to enhance the edge details and visual quality. Before this addition, we perform some spatial domain processing on the high pass images, based on hysteresis, to suppress the pixels which may not belong to the edges but retained in the high-pass image.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Banham, M.R., Katsaggelos, A.K.: Spatially Adaptive Wavelet-Based Multiscale Image Restoration. IEEE Transactions on Image Processing 5(4), 619–634 (1996)
Chand, R.H., Chan, T.C.: A Wavelet Algorithm for High Resolution Image Reconstruction. Society for Industrial and Applied Mathematics 24, 100–115 (1995)
Donoho, D.L., Raimondo, M.E.: A Fast Wavelet Algorithm for Image Deblurring. In: May, R., Roberts, A.J. (eds.) Proc. 12th Computational Techniques and Applications Conference, CTAC 2004, vol. 46, pp. C29–C46 (March 2005)
Donoho, D.L.: Nonlinear Solution of Linear Inverse Problems by Wavelet-Vaguelette Decomposition. Applied and Computational Harmonic Analysis 2, 101–126 (1992)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, Upper Saddle River, New Jersey, USA (2002)
Huang, M., Tseng, D., Liu, M.S.C.: Wavelet Image Enhancement Based on Teager Energy Operator. In: Proc. 16th International Conference on Pattern Recognition, vol. 2, pp. 993–996 (2002)
Jianhang, H., Jianzhong, Z.: Spatially Adaptive Image Deblurring Algorithm Based on Abdou Operator. In: Proc. 4th International Conference on Image and Graphics, ICIG 2007, pp. 67–70. IEEE Computer Society, Washington, DC (2007), http://dx.org/10.1109/ICIG.2007.172
Li, F., Fraser, D., Jia, X.: Wavelet Domain Deblurring and Denoising for Image Resolution Improvement. In: Proc. 9th Biennial Conference on Digital Image Computing Techniques and Applications, DICTA2007, pp. 373–379. Australian Pattern Recognition Society, Adelaide, Australia (December 2007)
Figueiredo, M.A.T., Nowak, R.D.: An EM Algorithm for Wavelet-Based Image Restoration. IEEE Transactions on Image Processing 12(8), 906–916 (2003)
Neelamani, R., Choi, H., Baraniuk, R.G.: ForWaRD: Fourier-Wavelet Regularized Deconvolution for Ill-Conditioned Systems. IEEE Transactions on Signal Processing 52(2), 418–433 (2004)
Ramponi, G., Polesel, A.: Rational Unsharp Masking Technique. Journal of Electronic Imaging 7(2), 333–338 (1998)
Tsai, D., Lee, Y.: A Method of Medical Image Enhancement using Wavelet-Coefficient Mapping Functions. In: Proc. of the International Conference on Neural Networks and Signal Processing, ICNNSP 2003, vol. 2, pp. 1091–1094 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Haq, N.u., Hayat, K., Noreen, N., Puech, W. (2011). Image Sharpening by DWT-Based Hysteresis. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-23687-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23686-0
Online ISBN: 978-3-642-23687-7
eBook Packages: Computer ScienceComputer Science (R0)