Spatially -Variant Roughness Penalty Design for Uniform Resolution in Penalized-Likelihood Image Reconstruction
dc.contributor.author | Stayman, J. Webster | en_US |
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.date.accessioned | 2011-08-18T18:21:01Z | |
dc.date.available | 2011-08-18T18:21:01Z | |
dc.date.issued | 1998-10-04 | en_US |
dc.identifier.citation | Stayman, J.W.; Fessler, J.A. (1998). "Spatially -Variant Roughness Penalty Design for Uniform Resolution in Penalized-Likelihood Image Reconstruction." International Conference on Image Processing 2: 685-689. <http://hdl.handle.net/2027.42/85891> | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/85891 | |
dc.description.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. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Spatially -Variant Roughness Penalty Design for Uniform Resolution in Penalized-Likelihood Image Reconstruction | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Biomedical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85891/1/Fessler150.pdf | |
dc.identifier.doi | 10.1109/ICIP.1998.723621 | en_US |
dc.identifier.source | International Conference on Image Processing | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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