Scene Text Image Super-Resolution via Parallelly Contextual Attention Network
Author:
Affiliation:
1. Tongji University, Shanghai, China
2. University of Southern California, Los Angeles, CA, USA
3. Fudan University, Shanghai, China
4. University of Electronic Science and Technology of China, Chengdu, China
Funder
Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science ?East China Normal University), Ministry of Education.
Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China (NSFC).
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3474085.3475469
Reference55 articles.
1. Marco Bevilacqua Aline Roumy Christine Guillemot and Marie Line Alberi-Morel. 2012. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. (2012). Marco Bevilacqua Aline Roumy Christine Guillemot and Marie Line Alberi-Morel. 2012. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. (2012).
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