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Text-image super-resolution through anchored neighborhood regression with multiple class-specific dictionaries

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Abstract

In the dictionary-based image super-resolution (SR) methods, the resolution of the input image is enhanced using a dictionary of low-resolution (LR) and high-resolution (HR) image patches. Typically, a single dictionary is learned from all the patches in the training set. Then, the input LR patch is super-resolved using its nearest LR patches and their corresponding HR patches in the dictionary. In this paper, we propose a text-image SR method using multiple class-specific dictionaries. Each dictionary is learned from the patches of images of a specific character in the training set. The input LR image is segmented into text lines and characters, and the characters are preliminarily classified. Likewise, overlapping patches are extracted from the input LR image. Then, each patch is super-resolved through the anchored neighborhood regression, using n class-specific dictionaries corresponding to the top-n classification results of the character containing the patch. The final HR image is generated by aggregating all the super-resolved patches. Our method achieves significant improvements in visual image quality and OCR accuracy, compared to the related dictionary-based SR methods. This confirms the effectiveness of applying the preliminary character classification results and multiple class-specific dictionaries in text-image SR.

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Notes

  1. Since the characters are at very low resolution, their classification results are not reliable. Therefore, the top-n predicted classes are utilized.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Ali Abedi & Ehsanollah Kabir

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  1. Ali Abedi
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  2. Ehsanollah Kabir
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Correspondence to Ali Abedi.

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Abedi, A., Kabir, E. Text-image super-resolution through anchored neighborhood regression with multiple class-specific dictionaries. SIViP 11, 275–282 (2017). https://doi.org/10.1007/s11760-016-0933-2

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  • DOI: https://doi.org/10.1007/s11760-016-0933-2

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