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Date
2023Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Deep learning-based medical image enhancement methods (e.g., denoising and super-resolution) mainly rely on paired data and correspondingly the well-trained models can only handle one type of task. In this paper, we address the limitation with a diffusion model-based framework that mitigates the requirement of paired data and can simultaneously handle multiple enhancement tasks by one pre-trained diffusion model without fine-tuning. Experiments on low-dose CT and heart MR datasets demonstrate that the proposed method is versatile and robust for image denoising and super-resolution. We believe our work constitutes a practical and versatile solution to scalable and generalizable image enhancement. Show more
Publication status
publishedExternal links
Editor
Book title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023Journal / series
Lecture Notes in Computer ScienceVolume
Pages / Article No.
Publisher
SpringerEvent
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ETH Bibliography
yes
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