Adaptive regularized constrained least squares image restoration
Berger, T.; Stromberg, J.O.; Eltoft, T.
Image Processing, IEEE Transactions on
Volume 8, Issue 9, Sep 1999 Page(s):1191 - 1203
Digital Object Identifier 10.1109/83.784432
Summary:In noisy environments, a constrained least-squares (CLS) approach
is presented to restore images blurred by a Gaussian impulse response,
where instead of choosing a global regularization parameter, each point
in the signal has its own associated regularization parameter. These
parameters are found by constraining the weighted standard deviation of
the wavelet transform coefficients on the finest scale of the inverse
signal by a function r which is a local measure of the intensity
variations around each point of the blurred and noisy observed signal.
Border ringing in the inverse solution is proposed decreased by
manipulating its wavelet transform coefficients on the finest scales
close to the borders. If the noise in the inverse solution is
significant, wavelet transform techniques are also applied to denoise
the solution. Examples are given for images, and the results are shown
to outperform the optimum constrained least-squares solution using a
global regularization parameter, both visually and in the mean squared
error sense
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