Context-Aware Optimal Transport Learning for Retinal Fundus Image Enhancement

• URL: http://arxiv.org/abs/2409.07862v1
• Date: Thu, 12 Sep 2024 09:14:37 GMT
• Title: Context-Aware Optimal Transport Learning for Retinal Fundus Image Enhancement
• Authors: Vamsi Krishna Vasa, Peijie Qiu, Wenhui Zhu, Yujian Xiong, Oana Dumitrascu, Yalin Wang,
• Abstract summary: This paper proposes a context-informed optimal transport (OT) learning framework for tackling unpaired fundus image enhancement. We derive the proposed context-aware OT using the earth's distance mover and show that the proposed context-OT has a solid theoretical guarantee. Experimental results on a large-scale dataset demonstrate the superiority of the proposed method over several state-of-the-art supervised and unsupervised methods.
• Score: 1.8339026473337505
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Retinal fundus photography offers a non-invasive way to diagnose and monitor a variety of retinal diseases, but is prone to inherent quality glitches arising from systemic imperfections or operator/patient-related factors. However, high-quality retinal images are crucial for carrying out accurate diagnoses and automated analyses. The fundus image enhancement is typically formulated as a distribution alignment problem, by finding a one-to-one mapping between a low-quality image and its high-quality counterpart. This paper proposes a context-informed optimal transport (OT) learning framework for tackling unpaired fundus image enhancement. In contrast to standard generative image enhancement methods, which struggle with handling contextual information (e.g., over-tampered local structures and unwanted artifacts), the proposed context-aware OT learning paradigm better preserves local structures and minimizes unwanted artifacts. Leveraging deep contextual features, we derive the proposed context-aware OT using the earth mover's distance and show that the proposed context-OT has a solid theoretical guarantee. Experimental results on a large-scale dataset demonstrate the superiority of the proposed method over several state-of-the-art supervised and unsupervised methods in terms of signal-to-noise ratio, structural similarity index, as well as two downstream tasks. The code is available at \url{https://github.com/Retinal-Research/Contextual-OT}.

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