Matches in UGent Biblio for { <https://biblio.ugent.be/publication/2118542#aggregation> ?p ?o. }
Showing items 1 to 37 of
37
with 100 items per page.
- aggregation classification "P1".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2012".
- aggregation format "application/pdf".
- aggregation hasFormat 2118542.bibtex.
- aggregation hasFormat 2118542.csv.
- aggregation hasFormat 2118542.dc.
- aggregation hasFormat 2118542.didl.
- aggregation hasFormat 2118542.doc.
- aggregation hasFormat 2118542.json.
- aggregation hasFormat 2118542.mets.
- aggregation hasFormat 2118542.mods.
- aggregation hasFormat 2118542.rdf.
- aggregation hasFormat 2118542.ris.
- aggregation hasFormat 2118542.txt.
- aggregation hasFormat 2118542.xls.
- aggregation hasFormat 2118542.yaml.
- aggregation isPartOf urn:isbn:9780819489623.
- aggregation isPartOf urn:issn:0277-786X.
- aggregation language "eng".
- aggregation publisher "SPIE, the International Society for Optical Engineering".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "Iterative CT reconstruction using shearlet-based regularization".
- aggregation abstract "In computerized tomography, it is important to reduce the image noise without increasing the acquisition dose. Extensive research has been done into total variation minimization for image denoising and sparse-view reconstruction. However, TV minimization methods show superior denoising performance for simple images (with little texture), but result in texture information loss when applied to more complex images. Since in medical imaging, we are often confronted with textured images, it might not be beneficial to use TV. Our objective is to find a regularization term outperforming TV for sparse-view reconstruction and image denoising in general. A recent efficient solver was developed for convex problems, based on a split-Bregman approach, able to incorporate regularization terms different from TV. In this work, a proof-of-concept study demonstrates the usage of the discrete shearlet transform as a sparsifying transform within this solver for CT reconstructions. In particular, the regularization term is the 1-norm of the shearlet coefficients. We compared our newly developed shearlet approach to traditional TV on both sparse-view and on low-count simulated and measured preclinical data. Shearlet-based regularization does not outperform TV-based regularization for all datasets. Reconstructed images exhibit small aliasing artifacts in sparse-view reconstruction problems, but show no staircasing effect. This results in a slightly higher resolution than with TV-based regularization.".
- aggregation authorList BK314898.
- aggregation volume "8313".
- aggregation aggregates 4381252.
- aggregation isDescribedBy 2118542.
- aggregation similarTo 12.911057.
- aggregation similarTo LU-2118542.