Local multiscale blur estimation based on toggle mapping for sharp region extraction

Abstract : In this paper a multiscale local blur estimation is proposed based on the existing local focus measure that combines gradient and toggle mapping. This method evaluates the quality of images regardless of their content (not in an autofocus context) and can predict OCR accuracy based on local blur. The resulting approach outperforms state of the art blur detection methods. Quantitative results are given on DIQA database. Moreover we demonstrate its usefulness for extracting a region of interest from partially blurry images. Results are shown on images acquired by a project devoted to smartphone based text extraction for visually impaired people. In this context sharp region extraction is essential since it allows warning the users when their picture is unusable. Moreover it saves computing time.
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Article dans une revue
IET Image Processing, Institution of Engineering and Technology, In press
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01851923
Contributeur : Beatriz Marcotegui <>
Soumis le : mardi 31 juillet 2018 - 11:58:50
Dernière modification le : lundi 12 novembre 2018 - 11:03:43
Document(s) archivé(s) le : jeudi 1 novembre 2018 - 13:23:14

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  • HAL Id : hal-01851923, version 1

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Luc Gillibert, Théodore Chabardès, Beatriz Marcotegui. Local multiscale blur estimation based on toggle mapping for sharp region extraction. IET Image Processing, Institution of Engineering and Technology, In press. 〈hal-01851923〉

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