OcrV1, France, Analysis, bibRecord, 000035

Efficient Example-Based Super-Resolution of Single Text Images Based on Selective Patch Processing

Identifieur interne : 000035 ( France/Analysis ); précédent : 000034; suivant : 000036

Efficient Example-Based Super-Resolution of Single Text Images Based on Selective Patch Processing

Auteurs : Nibal Nayef [France] ; Joseph Chazalon [France] ; Petra Gomez-Kr Mer [France] ; Jean-Marc Ogier [France]

Source :

RBID : Hal:hal-01315686

Abstract

Example-based super-resolution (SR) methods learn the correspondences between low resolution (LR) and high-resolution (HR) image patches, where the patches are extracted from a training database. To reconstruct a single LR image into a HR one, each LR image patch is processed by the previously trained model to recover its corresponding HR patch. For this reason, they are computationally inefficient. We propose the use of a selective patch processing technique to carry out the super-resolution step more efficiently, while maintaining the output quality. In this technique, only patches of high variance are processed by the costly reconstruction steps, while the rest of the patches are processed by fast bicubic interpolation. We have applied the proposed improvement on representative example-based SR methods to super-resolve text images. The results show a significant speed up for text SR without a drop in the ocr accuracy. In order to carry out an extensive and solid performance evaluation, we also present a public database of text images for training and testing example-based SR methods.

Url:

https://hal.archives-ouvertes.fr/hal-01315686

DOI: 10.1109/DAS.2014.25

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 000042
to stream Hal, to step Curation: 000042
to stream Hal, to step Checkpoint: 000036
to stream Main, to step Merge: 000076
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to stream France, to step Extraction: 000035

Links to Exploration step

Hal:hal-01315686

Le document en format XML

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<front><div type="abstract" xml:lang="en">Example-based super-resolution (SR) methods learn the correspondences between low resolution (LR) and high-resolution (HR) image patches, where the patches are extracted from a training database. To reconstruct a single LR image into a HR one, each LR image patch is processed by the previously trained model to recover its corresponding HR patch. For this reason, they are computationally inefficient. We propose the use of a selective patch processing technique to carry out the super-resolution step more efficiently, while maintaining the output quality. In this technique, only patches of high variance are processed by the costly reconstruction steps, while the rest of the patches are processed by fast bicubic interpolation. We have applied the proposed improvement on representative example-based SR methods to super-resolve text images. The results show a significant speed up for text SR without a drop in the ocr accuracy. In order to carry out an extensive and solid performance evaluation, we also present a public database of text images for training and testing example-based SR methods.</div>
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Efficient Example-Based Super-Resolution of Single Text Images Based on Selective Patch Processing

Efficient Example-Based Super-Resolution of Single Text Images Based on Selective Patch Processing

Source :

Abstract

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Links to Exploration step

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