Karhunen-Loeve multispectral and multiscale image restoration

Jean-Luc Starck; Philippe Querre; David L. Donoho

doi:10.1117/12.408662

4 December 2000 Karhunen-Loeve multispectral and multiscale image restoration

Jean-Luc Starck, Philippe Querre, David L. Donoho

Proceedings Volume 4119, Wavelet Applications in Signal and Image Processing VIII; (2000) https://doi.org/10.1117/12.408662
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

We introduce in this paper the notion of WT-KLT and apply it to the problem of noise removal. Decorrelating first the data in the spatial domain using the WT and afterwards using the KLT in spectral domain allows us to derive a roust noise modeling in the WT-KLT space, and hence to filter the transformed data in an efficient way. Experiments are performed in order to derive (i) the best way to calculate the covariance matrix in the case of noisy data, (ii) the best method to correct the noisy WT-KLT coefficients. Finally we investigate if the curvelet transform could be an alternative to the wavelet transform for color image filtering.

Citation Download Citation

Jean-Luc Starck, Philippe Querre, and David L. Donoho "Karhunen-Loeve multispectral and multiscale image restoration", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); https://doi.org/10.1117/12.408662

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KEYWORDS

Wavelets

Image filtering

Wavelet transforms

Data modeling

Signal to noise ratio

RGB color model

Astronomy

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