Prior Guided Fundus Image Quality Enhancement Via Contrastive Learning

Fundus images of poor quality may seriously influence clinic judgments. Existing fundus image quality enhancement (FIQE) approaches mainly make use of general image features but no prior domain knowledge. In this paper, we proposed and validated an efficient FIQE method with a prior constraint, name...

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Bibliographic Details
Main Authors: Cheng, Pujin, Lin, Li, Huang, Yijin, Lyu, Junyan, Tang, Xiaoying
Format: Conference Proceeding
Language:English
Subjects:
constrative learning
Fundus quality enhancement
Image quality
Network architecture
Optical design
Optical imaging
Optical losses
Pathology
Semantics
unpaired image translation
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Summary:Fundus images of poor quality may seriously influence clinic judgments. Existing fundus image quality enhancement (FIQE) approaches mainly make use of general image features but no prior domain knowledge. In this paper, we proposed and validated an efficient FIQE method with a prior constraint, named Efficient Prior Contrastive unpaired Generative Adversarial Network (EPC-GAN). Inspired by the contrastive unpaired translation framework, we emphasized local features during the enhancing process via contrastive patchwise samples. Moreover, to utilize high-level features in the fundus domain (such as vessels, optic disc/cup, and even lesions), we designed a fundus prior loss to avoid information modification and over-enhancement. Besides, we presented an efficient network architecture to overcome the high consumption in terms of both time and GPU-memory. Through both qualitative and quantitative experiments on a public dataset EyeQ, we demonstrated the superior performance of our proposed method.
ISSN:1945-8452
DOI:10.1109/ISBI48211.2021.9434005