提出自监督方法解决相位重构中的非线性难题
Self-supervised learning for phase retrieval
- 利用图像平移不变性构建自监督信号
- 仅需测量数据即可完成相位重构，无需高质量参考图
- 突破传统方法仅限于线性问题的限制
近年来，深度神经网络成为求解逆成像问题的有力工具。这些网络通常依赖成对图像进行训练：一组退化图像和对应的高质量图像（即‘真实标签’）。然而，在医学与科学成像中，全采样数据的缺乏限制了监督学习的应用。近期进展使得仅凭测量数据即可重建图像，不再需要参考图像。但这些方法仍局限于线性问题，无法处理如相位重构这类非线性问题。本文提出一种自监督方法，通过利用图像在平移下的自然不变性，克服了相位重构中的非线性限制。
原文摘要 · Abstract (English)
In recent years, deep neural networks have emerged as a solution for inverse imaging problems. These networks are generally trained using pairs of images: one degraded and the other of high quality, the latter being called 'ground truth'. However, in medical and scientific imaging, the lack of fully sampled data limits supervised learning. Recent advances have made it possible to reconstruct images from measurement data alone, eliminating the need for references. However, these methods remain limited to linear problems, excluding non-linear problems such as phase retrieval. We propose a self-supervised method that overcomes this limitation in the case of phase retrieval by using the natural invariance of images to translations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。