Abstract
A new shift-invariant non-aliasing Ridgelet transform was presented to avoid aliasing and shift-variant in old Ridgelet transform. Firstly, image size was expanded twice via interpolating zero values,so that the sampling rate in Radon transform domain was improved and the aliasing was reduced; Secondly, the one-dimensional discrete wavelet transform in the old Ridgelet transform was replaced by one-dimensional dual-tree Complex wavelet transform(DCWT) which had both shift-invariant and double directional analysis ability. On this basis, a new image enhancement method for linear features was proposed by using zero-mean Gaussian mode in the shift-invariant non-aliasing Ridgelet transform domain. Experimental result shows that the presented Ridgelet transform can avoid effectively the ‘scratch around’phenomenon in reconstructed image and the visual effects of the image enhancement is improved obviously.
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
Candes, E.J.: Monoscale ridgelets for the representation of images with edges. Stanford University, report (1999)
Hou, B., Liu, F., Jiao, L.: Line character detection based on Ridgelet transform. Science in China(Series E) 33(1), 65–73 (2003)
Xiao, X., Li, S.: Edge-preserving image denoising method using Curvelet transform. Journal of China Institute of Communications 25(2), 9–15 (2004)
Bai, J., Feng, X.: The digital ridgelet reconstruction based on dual frame. Science in China(Series E) 35(10), 1072–1082 (2005)
Kingsbury, N.G.: The dual-tree complex wavelet transform:a new technique for shift invariance and directional filters. In: Proceedings of the 8th IEEE Digital Signal Processing Workshop, Bryce Canyon UT, USA. IEEE Signal Processing Society (1998)
Cheng, S.G.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. on Image Processing 9(9), 1532–1546 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yan, H., Fu, Y., Yin, G. (2012). Line Feature Enhancement Using a New Shift-Invariant Non-aliasing Ridgelet Transform. In: Pan, Z., Cheok, A.D., Müller, W., Chang, M., Zhang, M. (eds) Transactions on Edutainment VII. Lecture Notes in Computer Science, vol 7145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29050-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-29050-3_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29049-7
Online ISBN: 978-3-642-29050-3
eBook Packages: Computer ScienceComputer Science (R0)