Abstract
There are lots of blank areas and similar or redundant characters in document image. To make use of these characteristics, we propose a weighted kernel regression super-resolution reconstruction model based on steering kernel regression and clustering learning methods in this paper. By this model, we can learn the local structure of characters and achieve document image super-resolution reconstruction. In our method, a large number of unrelated samples are used for local structure clustering, which make the reconstruction process can not only use structure information of local neighborhood, but also make use of lots of non-local neighborhood structure information learning from the cluster sub-sample sets. This proposed approach ensures robustness of reconstruction. Document image super-resolution experiments with subjective evaluation and objective indicators have proved the effectiveness of our method.
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Acknowledgments
We want to thank the help from the researchers and engineers of MicroPattern Corporation. This work is supported partially by China Postdoctoral Science Foundation (No: 2015M582355), the Doctor Scientific Research Start project from Hubei University of Science and Technology (No: BK1418) and the Team Plans Program of the Outstanding Young Science and Technology Innovation of Colleges and Universities in Hubei Province (T201513).
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Li, L., Liao, H., Chen, Y. (2016). Document Image Super-Resolution Reconstruction Based on Clustering Learning and Kernel Regression. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_6
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DOI: https://doi.org/10.1007/978-981-10-3005-5_6
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