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Local Consistency Constrained Adaptive Neighbor Embedding for Text Image Super-Resolution

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5 Author(s)
Wei Fan ; Fujitsu R&D Center Co., Ltd., Japan ; Jun Sun ; Naoi, S. ; Minagawa, A.
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This paper proposes a robust single-image super-resolution method for enlarging low quality camera captured text image. The contribution of this work is twofold. First, we point out the non-local reconstruction problem in neighbor embedding based super-resolution by statistical analysis on an empirical data set. Second, we introduce a local consistency constraint to explicitly regularize the linear reconstruction process, and adaptively generate the most possible candidates for the high-resolution image patch. For the non-consistent candidates, we rely on its adjacent overlapping patches for capability verification. Experimental results demonstrate that our solution produces visually pleasing enlargements for various text images.

Published in:

Document Analysis Systems (DAS), 2012 10th IAPR International Workshop on

Date of Conference:

27-29 March 2012