Joint super-resolution and compression artifact reduction based on dual-learning

Oh Young Lee, Jae Won Lee, Dae Yeol Lee, Jong-Ok Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We propose a novel integrated framework to combine the self-learning super-resolution (SR) with dual-learning noise-reduction (NR) for compressed images. Contrary to existing learning based denoising approach, dual-learning based joint SR and NR is proposed by adding a denoised training set. It makes the proposed framework more suitable for highly compressed noise by referring to closer patch in a training set. Also, it is robust for SR artifacts since the joint framework is designed in such a way that one could learn a process to simultaneously perform NR and SR. Experimental results show that the proposed joint SR and NR framework can achieve higher objective and subjective qualities, compared with individual processing of NR and SR.

Original languageEnglish
Title of host publicationVCIP 2016 - 30th Anniversary of Visual Communication and Image Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509053162
DOIs
Publication statusPublished - 2017 Jan 4
Event2016 IEEE Visual Communication and Image Processing, VCIP 2016 - Chengdu, China
Duration: 2016 Nov 272016 Nov 30

Other

Other2016 IEEE Visual Communication and Image Processing, VCIP 2016
CountryChina
CityChengdu
Period16/11/2716/11/30

Fingerprint

Noise abatement
Processing

Keywords

  • compression artifact reduction
  • Dual-learning
  • image super resolution
  • self-learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Lee, O. Y., Lee, J. W., Lee, D. Y., & Kim, J-O. (2017). Joint super-resolution and compression artifact reduction based on dual-learning. In VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing [7805543] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VCIP.2016.7805543

Joint super-resolution and compression artifact reduction based on dual-learning. / Lee, Oh Young; Lee, Jae Won; Lee, Dae Yeol; Kim, Jong-Ok.

VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing. Institute of Electrical and Electronics Engineers Inc., 2017. 7805543.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lee, OY, Lee, JW, Lee, DY & Kim, J-O 2017, Joint super-resolution and compression artifact reduction based on dual-learning. in VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing., 7805543, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE Visual Communication and Image Processing, VCIP 2016, Chengdu, China, 16/11/27. https://doi.org/10.1109/VCIP.2016.7805543
Lee OY, Lee JW, Lee DY, Kim J-O. Joint super-resolution and compression artifact reduction based on dual-learning. In VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing. Institute of Electrical and Electronics Engineers Inc. 2017. 7805543 https://doi.org/10.1109/VCIP.2016.7805543
Lee, Oh Young ; Lee, Jae Won ; Lee, Dae Yeol ; Kim, Jong-Ok. / Joint super-resolution and compression artifact reduction based on dual-learning. VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing. Institute of Electrical and Electronics Engineers Inc., 2017.
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