TY - GEN
T1 - Unsupervised real-world super resolution with cycle generative adversarial network and domain discriminator
AU - Kim, Gwantae
AU - Park, Jaihyun
AU - Lee, Kanghyu
AU - Lee, Junyeop
AU - Min, Jeongki
AU - Lee, Bokyeung
AU - Han, David K.
AU - Ko, Hanseok
N1 - Funding Information:
This material is based upon work supported by the Air Force Office of Scientific Research under award number FA2386-19-1-4001.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - This paper proposes an unsupervised single-image Super-Resolution(SR) model using cycleGAN and domain discriminator to solve the problem of SR with unknown degradation using unpaired dataset. In previous approaches, paired dataset is required for training with assumed levels of image degradation. In real world SR applications, however, training sets are typically not of low and high resolution image pairs, but only low resolution images with unknown degradation are provided as inputs. To address the problem, we introduce a cycle-in-cycle GAN based unsupervised learning model using an unpaired dataset. In addition, we combine several losses attributed to image contents, such as pixel-wise loss, VGG feature loss and SSIM loss, for stable learning and performance improvement. We also propose a domain discriminator, which consists of noise discriminator, texture discriminator and color discriminator, to guide generated images to follow target domain distribution rather than source domain. We validate effectiveness of our model in quantitative and qualitative experiments using NTIRE2020 real-world SR challenge dataset.
AB - This paper proposes an unsupervised single-image Super-Resolution(SR) model using cycleGAN and domain discriminator to solve the problem of SR with unknown degradation using unpaired dataset. In previous approaches, paired dataset is required for training with assumed levels of image degradation. In real world SR applications, however, training sets are typically not of low and high resolution image pairs, but only low resolution images with unknown degradation are provided as inputs. To address the problem, we introduce a cycle-in-cycle GAN based unsupervised learning model using an unpaired dataset. In addition, we combine several losses attributed to image contents, such as pixel-wise loss, VGG feature loss and SSIM loss, for stable learning and performance improvement. We also propose a domain discriminator, which consists of noise discriminator, texture discriminator and color discriminator, to guide generated images to follow target domain distribution rather than source domain. We validate effectiveness of our model in quantitative and qualitative experiments using NTIRE2020 real-world SR challenge dataset.
UR - http://www.scopus.com/inward/record.url?scp=85090164893&partnerID=8YFLogxK
U2 - 10.1109/CVPRW50498.2020.00236
DO - 10.1109/CVPRW50498.2020.00236
M3 - Conference contribution
AN - SCOPUS:85090164893
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 1862
EP - 1871
BT - Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PB - IEEE Computer Society
T2 - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
Y2 - 14 June 2020 through 19 June 2020
ER -