Abstract
Title : Reconstruction of wavelet coefficients using total variation minimization
Authors : Sylvain Durand and Jacques Froment
We propose a model to reconstruct wavelet coefficients using
a total variation minimization algorithm. The approach is motivated
by wavelet signal denoising methods, where thresholding small
wavelet coefficients leads pseudo-Gibbs artifacts. By replacing
these thresholded coefficients by values minimizing the total
variation, our method performs a nearly artifact free signal denoising.
In this paper, we detail the algorithm based on a subgradient
descent combining a projection on a linear space. The convergence of
the algorithm is established and numerical experiments are
reported.
AMS subject classification (MSC 2000) : 26A45, 65K10, 65T60, 94A12.