Fast reconstruction algorithm from modules maxima of signal wavelet transform and its application in enhancement of medical images

Guangtao Zhai; Fengrong Sun; Haohao Song; Mingqiang Zhang; Li Liu; Changyu Wang

doi:10.1117/12.538814

25 September 2003 Fast reconstruction algorithm from modules maxima of signal wavelet transform and its application in enhancement of medical images

Guangtao Zhai, Fengrong Sun, Haohao Song, Mingqiang Zhang, Li Liu, Changyu Wang

Author Affiliations +

Proceedings Volume 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition; (2003) https://doi.org/10.1117/12.538814
Event: Third International Symposium on Multispectral Image Processing and Pattern Recognition, 2003, Beijing, China

Abstract

The modulus maxima of a signal's wavelet transform on different levels contain important information of the signal, which can be help to construct wavelet coefficients. A fast algorithm based on Hermite interpolation polynomial for reconstructing signal from its wavelet transform maxima is proposed in this paper. An implementation of this algorithm in medical image enhancement is also discussed. Numerical experiments have shown that compared with the Alternating Projection algorithm proposed by Mallat, this reconstruction algorithm is simpler, more efficient, and at the same time keeps high reconstruction Signal to Noise Ratio. When applied to the image contract enhancement, the computing time of this algorithm is much less compared with the one using Mallat's Alternative Projection, and the results are almost the same, so it is a practical fast reconstruction algorithm.

Citation Download Citation

Guangtao Zhai, Fengrong Sun, Haohao Song, Mingqiang Zhang, Li Liu, and Changyu Wang "Fast reconstruction algorithm from modules maxima of signal wavelet transform and its application in enhancement of medical images", Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); https://doi.org/10.1117/12.538814

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