Super Resolution of Text Image by Pruning Outlier
Identifieur interne : 000433 ( Main/Exploration ); précédent : 000432; suivant : 000434Super Resolution of Text Image by Pruning Outlier
Auteurs : Ziye Yan [République populaire de Chine] ; Yao Lu [République populaire de Chine] ; Jianwu Li [République populaire de Chine]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: We propose a learning based super resolution algorithm for single frame text image. The distance based candidate of example can’t avoid the outliers and the super resolution result will be disturbed by the irrelevant outliers. In this work, the unique constraints of the text image are used to reject the outliers in the learning based SR algorithm. The final image is obtained by the Markov random field network with k nearest neighbor candidates from an image database that contains pairs of corresponding low resolution and high resolution text image patches. We demonstrate our algorithm on simulated and real scanned documents with promising results.
Url:
DOI: 10.1007/978-3-642-24965-5_73
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">Abstract: We propose a learning based super resolution algorithm for single frame text image. The distance based candidate of example can’t avoid the outliers and the super resolution result will be disturbed by the irrelevant outliers. In this work, the unique constraints of the text image are used to reject the outliers in the learning based SR algorithm. The final image is obtained by the Markov random field network with k nearest neighbor candidates from an image database that contains pairs of corresponding low resolution and high resolution text image patches. We demonstrate our algorithm on simulated and real scanned documents with promising results.</div>
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