AIRI denoiser shelves for PnP algorithms in high-dynamic range astronomical imaging

The dataset consists of non-expansive denoiser DNNs, underpinning the Plug-and-Play (PnP) algorithm AIRI for high-dynamic range astronomical imaging.

Two denoiser shelves are available, each composed of 8 DNNs, trained for the removal of random white Gaussian noise, with mean zero and a fixed standard deviation in the list {3.2E-4, 1.6E-4, 8E-5, 4E-5, 2E-5, 1E-5, 5E-6, 2.5E-6}:

1) airi_astro-based_oaid_shelf: trained from optical astronomical images (NOIRLab/NSF/AURA/H.Schweiker/WIYN/T.A.Rector (University of Alaska Anchorage));
2) airi_mri-based_mrid_shelf: trained from medical images (NYU fastMRI Initiative database; Knoll et al. 2020).

Two sets of denoisers are provided for uncertainty quantification of AIRI, each composed of 14 realisations of DNN denoisers trained for the removal of random white Gaussian noise, with mean zero and standard deviation 1.6E-4:

1) airi_astro-based_oaid_uncertainty_quantification: trained from optical astronomical images;
2) airi_astro-based_mrid_uncertainty_quantification: trained from medical images.

All DNNs are available in ONNX format. They can be deployed for arbitrary image sizes, subject to hardware memory constraints.

本数据集包含非扩张型去噪器深度神经网络（Deep Neural Network, DNN），用于支撑面向高动态范围天文成像的即插即用（Plug-and-Play, PnP）算法AIRI。

本数据集提供两类去噪器套件，每套均由8个DNN组成，这些DNN经过训练以去除均值为0、标准差取自集合{3.2×10^-4、1.6×10^-4、8×10^-5、4×10^-5、2×10^-5、1×10^-5、5×10^-6、2.5×10^-6}的随机白高斯噪声：
1. airi_astro-based_oaid_shelf：基于光学天文图像训练所得（数据来源：NOIRLab/NSF/AURA/H.Schweiker/WIYN/T.A.Rector（阿拉斯加安克雷奇大学））；
2. airi_mri-based_mrid_shelf：基于医学图像训练所得（数据来源：NYU fastMRI Initiative数据库；Knoll等人，2020年）。

本数据集还提供两组用于AIRI不确定性量化的去噪器，每组均由14个经训练的DNN去噪器复现版本组成，这些去噪器用于去除均值为0、标准差为1.6×10^-4的随机白高斯噪声：
1. airi_astro-based_oaid_uncertainty_quantification：基于光学天文图像训练所得；
2. airi_astro-based_mrid_uncertainty_quantification：基于医学图像训练所得。

所有DNN均采用开放神经网络交换（ONNX）格式提供，可部署适配任意图像尺寸，具体受限于硬件内存约束。