Scene Text Deblurring Using Text-Specific Multiscale Dictionaries

Keywords

multi-scale dictionaries; non-unifrom deblurring; Scene text; text localization

Abstract

Texts in natural scenes carry critical semantic clues for understanding images. When capturing natural scene images, especially by handheld cameras, a common artifact, i.e., blur, frequently happens. To improve the visual quality of such images, deblurring techniques are desired, which also play an important role in character recognition and image understanding. In this paper, we study the problem of recovering the clear scene text by exploiting the text field characteristics. A series of text-specific multiscale dictionaries (TMD) and a natural scene dictionary is learned for separately modeling the priors on the text and nontext fields. The TMD-based text field reconstruction helps to deal with the different scales of strings in a blurry image effectively. Furthermore, an adaptive version of nonuniform deblurring method is proposed to efficiently solve the real-world spatially varying problem. Dictionary learning allows more flexible modeling with respect to the text field property, and the combination with the nonuniform method is more appropriate in real situations where blur kernel sizes are depth dependent. Experimental results show that the proposed method achieves the deblurring results with better visual quality than the state-of-the-art methods.

Publication Date

4-1-2015

Publication Title

IEEE Transactions on Image Processing

Volume

24

Issue

4

Number of Pages

1302-1314

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TIP.2015.2400217

Socpus ID

84923567963 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84923567963

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