用引导流匹配提升荧光显微镜超分辨率，抗噪更强更可信。
ResMatching: Noise-Resilient Computational Super-Resolution via Guided Conditional Flow Matching
- 基于引导条件流匹配学习更强数据先验
- 在4个生物结构上优于7个基线，噪声下表现更优
- 可输出像素级不确定性，助用户识别不可靠预测
荧光显微镜中的计算超分辨率（CSR）虽为病态问题，但已有长期研究。其核心在于通过先验信息外推成像设备未捕获的高频内容。随着数据驱动机器学习技术发展，更优先验有望被学习，从而提升结果质量。本文提出ResMatching，一种基于引导条件流匹配的新CSR方法，用于学习改进的数据先验。我们在BioSR数据集的4个不同生物结构上评估该方法，并与7个基线对比。结果表明，ResMatching在所有情况下均实现最佳数据保真度与感知真实感权衡。尤其在低分辨率图像含强噪声、先验难学时，性能优势显著。此外，我们证明ResMatching可从隐式学习的后验分布中采样，且该分布对所有测试场景均校准良好，从而提供像素级数据不确定性估计，帮助用户排除不确定预测。
原文摘要 · Abstract (English)
Computational Super-Resolution (CSR) in fluorescence microscopy has, despite being an ill-posed problem, a long history. At its very core, CSR is about finding a prior that can be used to extrapolate frequencies in a micrograph that have never been imaged by the image-generating microscope. It stands to reason that, with the advent of better data-driven machine learning techniques, stronger prior can be learned and hence CSR can lead to better results. Here, we present ResMatching, a novel CSR method that uses guided conditional flow matching to learn such improved data-priors. We evaluate ResMatching on 4 diverse biological structures from the BioSR dataset and compare its results against 7 baselines. ResMatching consistently achieves competitive results, demonstrating in all cases the best trade-off between data fidelity and perceptual realism. We observe that CSR using ResMatching is particularly effective in cases where a strong prior is hard to learn, e.g. when the given low-resolution images contain a lot of noise. Additionally, we show that ResMatching can be used to sample from an implicitly learned posterior distribution and that this distribution is calibrated for all tested use-cases, enabling our method to deliver a pixel-wise data-uncertainty term that can guide future users to reject uncertain predictions.
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