Multiscale poisson data smoothing

@inproceedings{Jansen2003MultiscalePD,
  title={Multiscale poisson data smoothing},
  author={Maarten Jansen},
  year={2003}
}
The paper introduces a framework for non-linear multiscale decompositions of Poisson data that have piecewise smooth intensity curves. The key concept is conditioning on the sum of the observations that are involved in the computation of a given multiscale coefficient. Within this framework, most classical wavelet thresholding schemes for data with additive homoscedastic noise can be used. Any family of wavelet transforms (orthogonal, biorthogonal or second generation) can be incorporated in… CONTINUE READING

Figures and Tables from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 46 CITATIONS

Wavelets, Ridgelets, and Curvelets for Poisson Noise Removal

  • IEEE Transactions on Image Processing
  • 2008
VIEW 12 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Support value based stent-graft marker detection

  • Pattern Recognition
  • 2013
VIEW 9 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Optimization of variance-stabilizing transformations

VIEW 3 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

References

Publications referenced by this paper.
SHOWING 1-10 OF 32 REFERENCES

A gaussian cubature formula for the computation of generalized b-splines and its application to serial correlation

V. K. Kaishev
  • In Statistical multiple integration (Arcata, CA,
  • 1991
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL