Imaging is the process of transforming noisy, incomplete data into a space that humans can interpret. NIFTy is a Bayesian framework for imaging and has already successfully been applied to many fields in astrophysics. Previous design decisions held the performance and the development of methods in NIFTy back. We present a rewrite of NIFTy, coined NIFTy.re, which reworks the modeling principle, extends the inference strategies, and outsources much of the heavy lifting to JAX. The rewrite dramatically accelerates models written in NIFTy, lays the foundation for new types of inference machineries, improves maintainability, and enables interoperability between NIFTy and the JAX machine learning ecosystem.

将嘈杂、不完整的数据转换为人类可以解释的空间是成像的过程。NIFTy是一种贝叶斯成像框架，已成功应用于天体物理学的许多领域。我们提出了一个名为NIFTy.re的NIFTy重写版本，重新设计了建模原则，扩展了推断策略，并将大部分重要工作外包给了JAX。这个重写显著加速了使用NIFTy编写的模型，为新型推理机制奠定了基础，提高了可维护性，并实现了NIFTy与JAX机器学习生态系统之间的互操作性。

重新设想数值信息场理论（NIFTy.re）：高斯过程和变分推断库