Best wavelet packet basis for joint image deblurring-denoising and compression

Pierre Dherete; Sylvain Durand; Jacques Froment; Bernard Rouge

doi:10.1117/12.455786

30 January 2003 Best wavelet packet basis for joint image deblurring-denoising and compression

Pierre Dherete, Sylvain Durand, Jacques Froment, Bernard Rouge

Proceedings Volume 4793, Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications; (2003) https://doi.org/10.1117/12.455786
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

We propose a unique mathematical framework to deblur, denoise and compress natural images. Images are decomposed in a wavelet packet basis adapted both to the deblurring filter and to the denoising process. Effective denoising is performed by thresholding small wavelet packet coefficients while deblurring is obtained by multiplying the coefficients with a deconvolution kernel. This representation is compressed by quantizing the remaining coefficients and by coding the values using a context-based entropy coder. We present examples of such treatments on a satellite image chain. The results show a significant improvement compared to separate treatments with up-to-date compression approach.

Citation Download Citation

Pierre Dherete, Sylvain Durand, Jacques Froment, and Bernard Rouge "Best wavelet packet basis for joint image deblurring-denoising and compression", Proc. SPIE 4793, Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications, (30 January 2003); https://doi.org/10.1117/12.455786

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