26 October 1999 Multiscale shape analysis: beyond the normality and independence of noise
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In this paper, we propose a new algorithm for extracting a non smooth shape from its noisy observation. The key ideal is to project the noisy shape onto a set of orthogonal subspaces at different resolutions, and construct scale space representation gleaned from the locally smoothed shape. Using the curvature we proceed to filter the high resolution scale subspace by projecting it onto the scale which is in turn used for the reconstruction. Inspired by the conjugate mirror filter and the wavelet decomposition synergy, we propose a curvature based filter operating at different scales and with minimal knowledge about the noise statistics.
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Yun He, Yun He, A. Hamid Krim, A. Hamid Krim, } "Multiscale shape analysis: beyond the normality and independence of noise", Proc. SPIE 3813, Wavelet Applications in Signal and Image Processing VII, (26 October 1999); doi: 10.1117/12.366783; https://doi.org/10.1117/12.366783

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