Abstract
This paper presents anisotropic regularization techniques to exploit the piecewise smoothness of the image and the point spread function (PSF) in order to mitigate the severe lack of information encountered in blind restoration of shift-invariantly and shift-variantly blurred images. The new techniques, which are derived from anisotropic diffusion, adapt both the degree and direction of regularization to the spatial activities and orientations of the image and the PSF. This matches the piecewise smoothness of the image and the PSF which may be characterized by sharp transitions in magnitude and by the anisotropic nature of these transitions. For shift-variantly blurred images whose underlying PSF's may differ from one pixel to another, we parameterize the PSF and then apply the anisotropic regularization techniques. This is demonstrated for linear motion blur and out-of-focus blur. Alternating minimization is used to reduce the computational load and algorithmic complexity.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 396-407 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 8 |
| Issue number | 3 |
| DOIs | |
| State | Published - 1999 |
Bibliographical note
Funding Information:Manuscript received December 19, 1996; revised May 5, 1998. This work was supported in part by the BMDO/IST program managed by the Office of Naval Research, under Contract N00014-92-J-1911, and by the National Science Foundation under Grant CDA-9414015. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Stephen E. Reichenbach.
Keywords
- Anisotropic diffusion
- Blind restoration
- Image restoration
- Point spread function
- Regularization
- Shift-variant