研究人员正在开发新方法，利用扩散模型从有限数据中改进图像重建。一种方法通过结合现有模型，从单个观测中优化扩散先验，在黑洞成像等应用中显示出前景。另一种技术，条件扩散后验对齐（CDPA），通过将2D扩散模型条件化于初始3D重建并将其与测量投影对齐，实现了可扩展的3D稀疏视图CT重建。第三种方法DiffNR，通过使用名为SliceFixer的扩散模型来纠正伪影并提供辅助监督，从而增强稀疏视图CT的神经表示，提高了重建质量和效率。 AI
影响 有限数据图像重建中扩散模型优化的进展可能提高医学成像和科学观测的准确性。
排序理由 该集群包含多个arXiv预印本，详细介绍了使用扩散模型进行图像重建的新研究。
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arXiv:2508.02964v3 Announce Type: replace Abstract: Diffusion models have been firmly established as principled zero-shot solvers for linear and nonlinear inverse problems, owing to their powerful image prior and iterative sampling algorithm. These approaches often rely on Tweedi…
arXiv:2604.21066v2 Announce Type: replace Abstract: While diffusion priors generate high-quality posterior samples across many inverse problems, they are often trained on limited training sets or purely simulated data, thus inheriting the errors and biases of these underlying sou…
arXiv:2604.21960v1 Announce Type: cross Abstract: Computed Tomography (CT) is a widely used imaging modality in medical and industrial applications. To limit radiation exposure and measurement time, there is a growing interest in sparse-view CT, where the number of projection vie…
Neural representations (NRs), such as neural fields and 3D Gaussians, effectively model volumetric data in computed tomography (CT) but suffer from severe artifacts under sparse-view settings. To address this, we propose DiffNR, a novel framework that enhances NR optimization wit…