Robust super-resolution

Assaf Zomet*, Alex Rav-Acha, Shmuel Peleg

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

273 Scopus citations

Abstract

A robust approach for super resolution is presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion errors, inaccurate blur models, noise, moving objects, motion blur etc. This robustness is needed since super-resolution methods are very sensitive to such errors. A robust median estimator is combined in an iterative process to achieve a super resolution algorithm. This process can increase resolution even in regions with outliers, where other super resolution methods actually degrade the image.

Original languageEnglish
Pages (from-to)I645-I650
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
StatePublished - 2001
Event2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Kauai, HI, United States
Duration: 8 Dec 200114 Dec 2001

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