- Infrared imaging devices become more important for civil and military navigation. Noisy images are often a problem especially at poor visibility. Therefore denoising could improve the image quality by wavelet thresholding. Different popular threshold estimation methods are compared with regard to the hard-, soft-, firm- and non-negative garrote thresholding function. Experimental results show that the BayesShrink thresholding estimator applied on the non-negative garrote delivers the best results.
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References
D.L. Donoho: “Wavelet shrinkage and W.V.D.: A 10-minute tour” Proceedings International Conference on Wavelets and Applications, Toulouse, France , 1992
D.L. Donoho and I.M. Johnstone: “Ideal spatial adaption via wavelet shrinkage” Biometrica, Vol. 81, pp. 425-455, 1994
D.L. Donoho and I.M. Johnstone: “Adapting to unknown smoothness via wavelet shrinkage” Journal of American Statistical Association, Vol. 90, No. 432, pp. 1200-1224, 1995
D.L. Donoho and I.M. Johnstone: “Wavelet shrinkage: Asymptopia?” Journal of the Royal Statistical Society Series B 57, pp. 301-369, 1995
D.L. Donoho: “De-Noising by Soft-Thresholding” IEEE Transactions on Information Theory, Vol. 41, No. 3, pp. 613-627, 1995
S. Mallat and S. Zhong: “Characterization of Signals from Multiscale Edges” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 7, pp. 710-732, 1992
S. Mallat and W.-L. Hwang: “Singularity Detection and Processing with Wavelets” IEEE Transactions on Information Theory, Vol. 38, No. 2, pp. 617-643, 1992
I. Koren and A. Laine: “A Discrete Dyadic Wavelet Transform for Multidimensional Feature Analysis” Time-Frequency and Wavelet Transform in Biomedical Engineering, M. Akay (Editor), IEEE Press, New York, 1997
A.G. Bruce, H.Y. Gao: “Understanding WaveShrink: Variance and Bias Estimation” Research Report, No. 36, StatSci Division of MathSoft, 1996
A.G. Bruce, H.Y. Gao: “WaveShrink with Firm Shrinkage” Research Report, No. 39, StatSci Division of MathSoft, 1996
H.Y. Gao: “Wavelet shrinkage denoising using the non-negative garrote” Journal of Computational and Graphical Statistics, Vol. 7, No. 4, pp. 469-488, 1998
S.G. Chang, B. Yu, M. Vetterli: “Adaptive Wavelet Thresholding for Image Denoising ans Compression” IEEE Transaction on Image Processing, Vol. 9, No. 9, pp. 1532-1546, 2000
M. Jansen: “Wavelet Thresholding and Noise Reduction” PHD Thesis, Department of Computer Science, KULeuven (Leuven, Belgium), 2000
G.Y. Chen, T.D. Bui, A. Krzyzak: “Image denoising using neighbouring wavelet coefficients” Proceedings of ICASSP 2004, Vol. 2, pp. 917-920, 2004
W. Shengqian, Z. Yuanhua, Z. Daowen: “Adaptive shrinkage de-noising using neighbourhood characteristic” Electronics Letters, Vol. 38, No. 11, pp.502-503, 2002
S.G. Chang, B. Yu, M. Vetterli: “Spatially adaptive wavelet thresholding with context modeling for image denoising” Proceedings of International Conference on Image Processing, Vol. 1, pp.535-539, 1998
S.G. Chang, B. Yu, M. Vetterli: “Spatially Adaptive Wavelet Thresholding with Context Modeling for Image Denoising” IEEE Transactions on Image Processing, Vol. 9, No. 9, pp.1522-1531, 2000
M.K. Mihacak, I. Kozintsev, K. Ramchandran: “Low-Complexity Image Denoising Based on Statistical Modeling of Wavelet Coefficients” IEEE Signal Processing Letters, Vol. 6, No. 12, pp. 300-303, 1999
M.K. Mihacak, I. Kozintsev, K. Ramchandran: “Spatially adaptive statistical Modeling of wavelet image Coefficients and its application to denoising” Proceedings of ICASSP, Vol. 6, pp. 3253-3256, 1999
L. Sendur, I.W. Selesnick: “Bivariate Shrinkage with local variance estimation” IEEE Signal Processing Letters, Vol. 9, No. 12, pp. 438-441, 2002
Y. Xu, J.B. Weaver, D.M. Healy, J. Lu: “Wavelet Transform Domain Filters: A Spatially Selective Noise Filtration Technique” IEEE Transaction on Image Processing, Vol. 3, No. 6, pp. 747-758, 1994
T.-C. Hsung, D. P.-K. Lun, W.-C. Siu: “Denoising by Singularity Detection” IEEE Transaction on Signal Processing, Vol. 47, No. 11, pp. 3139-3144, 1999
J. Scharcanski, C.R. Jung, R.T. Clarke: “Adaptive Image Denoising using Scale and Space Consistency” IEEE Transaction on Image Processing, Vol. 11, No. 9, pp. 1092-1101, 2002
Mike Wakin: “Standard Test Images” Webpage, http://www.ece.rice.edu/∼ wakin/images , 2005
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Wippig, D., Klauer, B., Zeidler, H.C. (2007). Denoising of Infrared Images by Wavelet Thresholding. In: Elleithy, K., Sobh, T., Mahmood, A., Iskander, M., Karim, M. (eds) Advances in Computer, Information, and Systems Sciences, and Engineering. Springer, Dordrecht. https://doi.org/10.1007/1-4020-5261-8_18
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