论文标题
noings2ASTRO：自我监督的神经网络的天文图像降级
Noise2Astro: Astronomical Image Denoising With Self-Supervised NeuralNetworks
论文作者
论文摘要
在观察性天文学中，噪声掩盖了感兴趣的信号。大规模的天文调查的规模和复杂性正在增长，这将产生更多数据并增加数据处理的工作量。开发自动化工具，例如卷积神经网络（CNN），以替代已成为一个有希望的研究领域。我们研究了基于CNN的自制学习算法的可行性（例如，噪声2Noise）来降低天文图像。我们在模拟嘈杂的天文数据上尝试了Noise2noise。我们根据恢复通量和形态的准确性评估结果。该算法可以很好地恢复泊松噪声的通量（$ 98.13 $ {\ rishbox {0.5ex} {\ tiny $^{+0.77} _ { - 0.90} $ \ giald \％$}），当图像数据具有光滑的信号配置文件时（$ 96.45 $ {\ rishbox {0.5ex} {\ tiny $^{+0.80} _ { - 0.96} $} $ \ groon \％$}）。
In observational astronomy, noise obscures signals of interest. Large-scale astronomical surveys are growing in size and complexity, which will produce more data and increase the workload of data processing. Developing automated tools, such as convolutional neural networks (CNN), for denoising has become a promising area of research. We investigate the feasibility of CNN-based self-supervised learning algorithms (e.g., Noise2Noise) for denoising astronomical images. We experimented with Noise2Noise on simulated noisy astronomical data. We evaluate the results based on the accuracy of recovering flux and morphology. This algorithm can well recover the flux for Poisson noise ( $98.13${\raisebox{0.5ex}{\tiny$^{+0.77}_{-0.90} $}$\large\%$}) and for Gaussian noise when image data has a smooth signal profile ($96.45${\raisebox{0.5ex}{\tiny$^{+0.80}_{-0.96} $}$\large\%$}).
