Abstract
We show in this paper that the average over translations of an operator diagonal in a wavelet packet basis is a convolution. We also show that an operator diagonal in a wavelet packet basis can be decomposed into several operators of the same kind, each of them being better conditioned. We investigate the possibility of using such a convolution to approximate a given convolution (in practice an image blur). Then we use these approximations to deblur images. First, we show that this framework permits us to redefine existing deblurring methods. Then, we show that it permits us to define a new variational method which combines the wavelet packet and the total variation approaches. We argue and show by experiments that this permits us to avoid the drawbacks of both approaches which are, respectively, the ringing and the staircasing.
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