Hardware-friendly Scalable Image Super Resolution with Progressive Structured Sparsity
Author:
Affiliation:
1. School of Informatics, Xiamen University, Xiamen, China
2. Lenovo Research, Beijing, China
3. East China Normal University & Chongqing Institute of East China Normal University, Shanghai & Chongqing, China
Funder
CCFLenovo Blue Ocean Research Fund
National Natural Science Foundation of China
Natural Science Foundation of Chongqing
National Key Research and Development Program of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3581783.3611875
Reference42 articles.
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2. Contour Detection and Hierarchical Image Segmentation
3. 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).
4. Bohong Chen , Mingbao Lin , Kekai Sheng , Mengdan Zhang , Peixian Chen , Ke Li , Liujuan Cao , and Rongrong Ji . 2022 b. ARM: Any-Time Super-Resolution Method. arXiv preprint arXiv:2203.10812 (2022). Bohong Chen, Mingbao Lin, Kekai Sheng, Mengdan Zhang, Peixian Chen, Ke Li, Liujuan Cao, and Rongrong Ji. 2022b. ARM: Any-Time Super-Resolution Method. arXiv preprint arXiv:2203.10812 (2022).
5. Chengpeng Chen , Zichao Guo , Haien Zeng , Pengfei Xiong , and Jian Dong . 2022a. RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization. arXiv preprint arXiv:2211.06088 ( 2022 ). Chengpeng Chen, Zichao Guo, Haien Zeng, Pengfei Xiong, and Jian Dong. 2022a. RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization. arXiv preprint arXiv:2211.06088 (2022).
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