Epitomic Image Super-Resolution

Authors

  • Yingzhen Yang University of Illinois at Urbana-Champaign
  • Zhangyang Wang University of Illinois at Urbana-Champaign
  • Zhaowen Wang Adobe Research
  • Shiyu Chang University of Illinois at Urbana-Champaign
  • Ding Liu University of Illinois at Urbana-Champaign
  • Honghui Shi University of Illinois at Urbana-Champaign
  • Thomas Huang University of Illinois at Urbana-Champaign

DOI:

https://doi.org/10.1609/aaai.v30i1.9920

Abstract

We propose Epitomic Image Super-Resolution (ESR) to enhance the current internal SR methods that exploit the self-similarities in the input. Instead of local nearest neighbor patch matching used in most existing internal SR methods, ESR employs epitomic patch matching that features robustness to noise, and both local and non-local patch matching. Extensive objective and subjective evaluation demonstrate the effectiveness and advantage of ESR on various images.

AAAI 2016 Proceedings Cover

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Published

2016-03-05

How to Cite

Yang, Y., Wang, Z., Wang, Z., Chang, S., Liu, D., Shi, H., & Huang, T. (2016). Epitomic Image Super-Resolution. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9920

Issue

Vol. 30 No. 1 (2016): Thirtieth AAAI Conference on Artificial Intelligence

Section

Student Abstracts and Posters