标题： 利用深度生成先验学习夜空 显示英文标题

标题： Learning the Night Sky with Deep Generative Priors

摘要： 从模糊的观测中恢复更清晰的图像（称为反卷积）是一个不适定问题，经典的解决方案常常产生不理想的结果。 在地面天文学中，由于大气效应导致不同曝光之间的点扩散函数发生变化，这使得结合多个曝光以获得具有更高信噪比的图像变得复杂。 我们开发了一种基于深度生成先验的无监督多帧方法，用于去噪、去模糊以及叠加图像。 我们使用一种精心设计的卷积神经网络架构，该架构结合了来自多次观测的信息，正则化这些观测的联合似然，并允许我们施加所需约束，例如尖锐恢复图像中像素值的非负性。 着眼于鲁宾天文台，我们分析了 4K×4K 的超广角巡天相机 (Hyper Suprime-Cam) 曝光数据，并获得了初步结果，得到了有前景的恢复图像和提取的源列表。 显示英文摘要

摘要： Recovering sharper images from blurred observations, referred to as deconvolution, is an ill-posed problem where classical approaches often produce unsatisfactory results. In ground-based astronomy, combining multiple exposures to achieve images with higher signal-to-noise ratios is complicated by the variation of point-spread functions across exposures due to atmospheric effects. We develop an unsupervised multi-frame method for denoising, deblurring, and coadding images inspired by deep generative priors. We use a carefully chosen convolutional neural network architecture that combines information from multiple observations, regularizes the joint likelihood over these observations, and allows us to impose desired constraints, such as non-negativity of pixel values in the sharp, restored image. With an eye towards the Rubin Observatory, we analyze 4K by 4K Hyper Suprime-Cam exposures and obtain preliminary results which yield promising restored images and extracted source lists.