TY - GEN
T1 - Domain Generalization in Restoration of Cataract Fundus Images Via High-Frequency Components
AU - Liu, Haofeng
AU - Li, Heng
AU - Ou, Mingyang
AU - Zhao, Yitian
AU - Qi, Hong
AU - Hu, Yan
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Cataracts are the most common blinding disease, and also impact the observation of the fundus. To boost the fundus examination of cataract patients, restoration algorithms have been proposed to address the degradation of fundus images caused by cataracts. However, it is impractical in clinics to collect paired or annotated fundus images for developing restoration models. In this paper, a restoration algorithm is designed for cataractous images without paired or annotated data. Domain generalization (DG) is applied to learn domain-invariant features (DIFs) from synthesized data, and the high-frequency components (HFCs) are extracted to conduct domain alignment. The proposed algorithm is used on unseen target data in the experiments. The effectiveness of the algorithm is demonstrated in the ablation study and compared with state-of-the-art methods. The code of this paper will be released at https://github.com/HeverLaw/Restoration-of-Cataract-Images-via-Domain-Generalization.
AB - Cataracts are the most common blinding disease, and also impact the observation of the fundus. To boost the fundus examination of cataract patients, restoration algorithms have been proposed to address the degradation of fundus images caused by cataracts. However, it is impractical in clinics to collect paired or annotated fundus images for developing restoration models. In this paper, a restoration algorithm is designed for cataractous images without paired or annotated data. Domain generalization (DG) is applied to learn domain-invariant features (DIFs) from synthesized data, and the high-frequency components (HFCs) are extracted to conduct domain alignment. The proposed algorithm is used on unseen target data in the experiments. The effectiveness of the algorithm is demonstrated in the ablation study and compared with state-of-the-art methods. The code of this paper will be released at https://github.com/HeverLaw/Restoration-of-Cataract-Images-via-Domain-Generalization.
KW - Cataract
KW - domain generalization
KW - domain-invariant features
KW - image restoration
UR - http://www.scopus.com/inward/record.url?scp=85129636923&partnerID=8YFLogxK
U2 - 10.1109/ISBI52829.2022.9761606
DO - 10.1109/ISBI52829.2022.9761606
M3 - Conference contribution
AN - SCOPUS:85129636923
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - ISBI 2022 - Proceedings
PB - IEEE Computer Society
T2 - 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
Y2 - 28 March 2022 through 31 March 2022
ER -