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 language | English |
|---|---|
| Title of host publication | VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509053162 |
| DOIs | |
| Publication status | Published - 2017 Jan 4 |
| Event | 2016 IEEE Visual Communication and Image Processing, VCIP 2016 - Chengdu, China Duration: 2016 Nov 27 → 2016 Nov 30 |
Other
| Other | 2016 IEEE Visual Communication and Image Processing, VCIP 2016 |
|---|---|
| Country | China |
| City | Chengdu |
| Period | 16/11/27 → 16/11/30 |
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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
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 proceeding › Conference contribution
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TY - GEN
T1 - Joint super-resolution and compression artifact reduction based on dual-learning
AU - Lee, Oh Young
AU - Lee, Jae Won
AU - Lee, Dae Yeol
AU - Kim, Jong-Ok
PY - 2017/1/4
Y1 - 2017/1/4
N2 - 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.
AB - 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.
KW - compression artifact reduction
KW - Dual-learning
KW - image super resolution
KW - self-learning
UR - http://www.scopus.com/inward/record.url?scp=85011072032&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011072032&partnerID=8YFLogxK
U2 - 10.1109/VCIP.2016.7805543
DO - 10.1109/VCIP.2016.7805543
M3 - Conference contribution
AN - SCOPUS:85011072032
BT - VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing
PB - Institute of Electrical and Electronics Engineers Inc.
ER -