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Review: Deep Laplacian Pyramid Networks for Single Image Super-Resolution

Jun 8, 2017 — 2 min read

Brian Jaehong Aum

이 포스팅은 루닛 블로그에 2017년 6월에 올렸던 포스트입니다.


원문 링크: https://blog.lunit.io/2017/06/08/deep-laplacian-pyramid-networks-for-single-image-super-resolution/


(본 포스팅은 Medium이 latex 함수를 지원하지 않아 이전 포스팅을 PDF로 업로드 하였습니다. 깔끔하게 보시기 원하시는 분들은 원문 참조 부탁드리겠습니다.)











Reference


  1. W. S. Lai, J. B. Huan, N. Ahuja, and M. H. Yang. Deep Laplacian pyramid networks for fast and accurate super-resolution. CVPR, 2017, accepted

  2. C. Dong, C. C. Loy, K. He, and X. Tang. Image super- resolution using deep convolutional networks. TPAMI, 38(2):295–307, 2015.

  3. C. Dong, C. C. Loy, and X. Tang. Accelerating the super- resolution convolutional neural network. In ECCV, 2016.

  4. J. Kim, J. K. Lee, and K. M. Lee. Accurate image super- resolution using very deep convolutional networks. In CVPR, 2016.

  5. J. Kim, J. K. Lee, and K. M. Lee. Deeply-recursive convolu- tional network for image super-resolution. In CVPR, 2016.

AIComputer VisionDeep LearningLunitMachine LearningResearchSuper Resolution딥러닝루닛연구

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