Abstract
Traditional space-invariant regularization schemes in tomographic image reconstruction using penalized-likelihood estimators produce images with nonuniform resolution properties. The local point spread functions that quantify the local smoothing properties of such estimators are not only space-variant and asymmetric, but are also object-dependent even for space-invariant systems. We propose a new regularization scheme for increased spatial uniformity and demonstrate the resolution properties of this new method versus conventional regularization schemes through an investigation of local point spread functions.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE International Conference on Image Processing |
| Publisher | IEEE Comp Soc |
| Pages | 685-689 |
| Number of pages | 5 |
| Volume | 2 |
| State | Published - 1998 |
| Externally published | Yes |
| Event | Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) - Chicago, IL, USA Duration: Oct 4 1998 → Oct 7 1998 |
Other
| Other | Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) |
|---|---|
| City | Chicago, IL, USA |
| Period | 10/4/98 → 10/7/98 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Electrical and Electronic Engineering
Cite this
Spatially-variant roughness penalty design for uniform resolution in penalized-likelihood image reconstruction. / Stayman, Joseph Webster; Fessler, Jeffrey A.
IEEE International Conference on Image Processing. Vol. 2 IEEE Comp Soc, 1998. p. 685-689.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Spatially-variant roughness penalty design for uniform resolution in penalized-likelihood image reconstruction
AU - Stayman, Joseph Webster
AU - Fessler, Jeffrey A.
PY - 1998
Y1 - 1998
N2 - Traditional space-invariant regularization schemes in tomographic image reconstruction using penalized-likelihood estimators produce images with nonuniform resolution properties. The local point spread functions that quantify the local smoothing properties of such estimators are not only space-variant and asymmetric, but are also object-dependent even for space-invariant systems. We propose a new regularization scheme for increased spatial uniformity and demonstrate the resolution properties of this new method versus conventional regularization schemes through an investigation of local point spread functions.
AB - Traditional space-invariant regularization schemes in tomographic image reconstruction using penalized-likelihood estimators produce images with nonuniform resolution properties. The local point spread functions that quantify the local smoothing properties of such estimators are not only space-variant and asymmetric, but are also object-dependent even for space-invariant systems. We propose a new regularization scheme for increased spatial uniformity and demonstrate the resolution properties of this new method versus conventional regularization schemes through an investigation of local point spread functions.
UR - http://www.scopus.com/inward/record.url?scp=0032295653&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0032295653&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0032295653
VL - 2
SP - 685
EP - 689
BT - IEEE International Conference on Image Processing
PB - IEEE Comp Soc
ER -