OcrV1, Hal, Corpus, bibRecord, 000038

Deblurring of Document Images Based on Sparse Representations Enhanced by Non-local Means

Identifieur interne : 000038 ( Hal/Corpus ); précédent : 000037; suivant : 000039

Deblurring of Document Images Based on Sparse Representations Enhanced by Non-local Means

Auteurs : Nibal Nayef ; Petra Gomez-Kr Mer ; Jean-Marc Ogier

Source :

RBID : Hal:hal-01315620

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:

https://hal.archives-ouvertes.fr/hal-01315620

DOI: 10.1109/ICPR.2014.760

Links to Exploration step

Hal:hal-01315620

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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<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.</abstract>
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Deblurring of Document Images Based on Sparse Representations Enhanced by Non-local Means

Deblurring of Document Images Based on Sparse Representations Enhanced by Non-local Means

Source :

Abstract

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri