Deblurring of Document Images Based on Sparse Representations Enhanced by Non-local Means
Identifieur interne : 000064 ( Main/Exploration ); précédent : 000063; suivant : 000065Deblurring of Document Images Based on Sparse Representations Enhanced by Non-local Means
Auteurs : Nibal Nayef [France] ; Petra Gomez-Kr Mer [France] ; Jean-Marc Ogier [France]Source :
Abstract
Blur is one of the most difficult distortions incamera captured documents. It degrades the visual quality of animage, and makes it difficult to read whether by a human or OCRsystems. This paper presents a novel non-blind deblurring methodthat combines the well known effective techniques of sparse representations and non-local image similarity. The presented problemformulation enables the use of standard sparse coding methodsfor solving sparse coding-based deblurring when enhanced bya non-local means prior. The method has been tested on bothsynthetic and real document images degraded with a variety ofblur kernels. The resulting deblurred images have high qualityin terms of both signal-to-noise ratio and OCR accuracy.
Url:
DOI: 10.1109/ICPR.2014.760
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">Blur is one of the most difficult distortions incamera captured documents. It degrades the visual quality of animage, and makes it difficult to read whether by a human or OCRsystems. This paper presents a novel non-blind deblurring methodthat combines the well known effective techniques of sparse representations and non-local image similarity. The presented problemformulation enables the use of standard sparse coding methodsfor solving sparse coding-based deblurring when enhanced bya non-local means prior. The method has been tested on bothsynthetic and real document images degraded with a variety ofblur kernels. The resulting deblurred images have high qualityin terms of both signal-to-noise ratio and OCR accuracy.</div>
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