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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 416-428 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 5 |
| Issue number | 3 |
| DOIs | |
| State | Published - 1996 |
Bibliographical note
Funding Information:Manuscript received August 26, 1994; revised June 26, 1995. This work was supported by the BMDO/IST program managed by the Office of Naval Research under Contract N00014-92-J-1911. The associate editor coordinating the review of this paper and approving it for publication was Prof. Xinhua Zhuang. The authors are with the Department of Electrical Engineering, University of Minnesota, Minneapolis, MN 55455 USA (email: kaveh@ee.umn.edu). Publisher Item Identifier S 1057-7 149(96)0 1802-7.