计算机科学
人工智能
组分(热力学)
监督学习
眼底(子宫)
模式识别(心理学)
无监督学习
图像(数学)
像素
质量(理念)
计算机视觉
机器学习
人工神经网络
物理
哲学
眼科
认识论
热力学
医学
作者
Pujin Cheng,Li Lin,Yijin Huang,Junyan Lyu,Xiaoying Tang
标识
DOI:10.1007/978-3-030-87237-3_9
摘要
Fundus image quality is crucial for screening various ophthalmic diseases. In this paper, we proposed and validated a novel fundus image enhancement method, named importance-guided semi-supervised contrastive constraining (I-SECRET). Specifically, our semi-supervised framework consists of an unsupervised component, a supervised component, and an importance estimation component. The unsupervised part makes use of a large publicly-available dataset of unpaired high-quality and low-quality images via contrastive constraining, whereas the supervised part utilizes paired images through degrading pre-selected high-quality images. The importance estimation provides a pixel-wise importance map to guide both unsupervised and supervised learning. Extensive experiments on both authentic and synthetic data identify the superiority of our proposed method over existing state-of-the-art ones, both quantitatively and qualitatively.
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