A regularization approach to joint blur identification and imagerestoration
Yu-Li You
Kaveh, M.
Dept. of Electr. Eng., Minnesota Univ., Minneapolis, MN;
This paper appears in: Image Processing, IEEE Transactions on
Publication Date: Mar 1996
Volume: 5,
Issue: 3
On page(s): 416-428
ISSN: 1057-7149
References Cited: 26
CODEN: IIPRE4
INSPEC Accession Number: 5253132
Digital Object Identifier: 10.1109/83.491316
Current Version Published: 2002-08-06
Abstract
The primary difficulty with blind image restoration, or joint blur
identification and image restoration, is insufficient information. This
calls for proper incorporation of a priori knowledge about the image and
the point-spread function (PSF). A well-known space-adaptive
regularization method for image restoration is extended to address this
problem. This new method effectively utilizes, among others, the
piecewise smoothness of both the image and the PSF. It attempts to
minimize a cost function consisting of a restoration error measure and
two regularization terms (one for the image and the other for the blur)
subject to other hard constraints. A scale problem inherent to the cost
function is identified, which, if not properly treated, may hinder the
minimization/blind restoration process. Alternating minimization is
proposed to solve this problem so that algorithmic efficiency as well as
simplicity is significantly increased. Two implementations of
alternating minimization based on steepest descent and conjugate
gradient methods are presented. Good performance is observed with
numerically and photographically blurred images, even though no
stringent assumptions about the structure of the underlying blur
operator is made
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