P.L. Dragotti and M. Vetterli. Wavelet transform footprints: Catching singularities for compression and denoising. In IEEE Int. Conf. on Image Processing, Vancouver, Canada, September 2000. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Wavelet Footprints and Frames for Signal Processing and.. - Dragotti (2002) Self-citation (Dragotti) (Correct)
No context found.
P.L. Dragotti and M. Vetterli. Wavelet transform footprints: Catching singularities for compression and denoising. In IEEE Int. Conf. on Image Processing, Vancouver, Canada, September 2000.
Wavelets, Approximation, and Compression - Vetterli (2001) (5 citations) Self-citation (Vetterli) (Correct)
No context found.
P.L. Dragotti and M. Vetterli, "Wavelet transform footprints: Catching singularities for compression and denoising," in Proc. IEEE Int. Conf. Image Processing, ICIP 2000.
Wavelet Footprints and Frames for Signal Processing and.. - Dragotti (2002) Self-citation (Dragotti) (Correct)
No context found.
P.L. Dragotti and M. Vetterli. Wavelet transform footprints: Catching singularities for compression and denoising. In IEEE Int. Conf. on Image Processing, Vancouver, Canada, September 2000.
Footprints And Edgeprints For Image Denoising And Compression - Dragotti, Vetterli (2001) (4 citations) Self-citation (Dragotti Vetterli) (Correct)
....of arbitrary signals is a central problem in signal processing applications. Wavelets, for instance, are known to be good approximants for 1 D piecewise smooth signals. The choice of a good basis, however, is only one of the elements that makes an efficient compression algorithm. In a recent work [3], we have introduced a compression algorithm that jointly uses wavelets and footprints. This algorithm has the right R D behaviour for compression of piecewise polynomial signals. Footprint is a data structure that contains all the non zero wavelet coefficients generated by a singularity. It is ....
....to geometrical wavelets introduced in [5] As footprints in 1 D, edgeprints allow to take advantage of the dependency between the wavelet coefficients generated by an edge in an image. This is an important condition to get an efficient 2 D compression algorithm. 2. THE FOOTPRINT EXPANSION In [3], we have introduced the notion of footprints and proposed their use for compression or denoising of piecewise polynomial signals. What is interesting is that footprints can be seen as an overcomplete basis for representation of piecewise polynomial functions. Consider a discrete time piecewise ....
P.L. Dragotti and M. Vetterli "Wavelet transform footprints: catching singularities for compression and denoising", Int. Conf. on Image Processing, Vancouver, September 2000.
Foveal Detection and Approximation for Singularities - Mallat (2003) (Correct)
No context found.
P.L. Dragotti and M. Vetterli. Wavelet transform footprints: catching singularities for compression and denoising. In Inter. Conf. on Image Processing, Vancouver, 2000.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC