Noise2Astro: Astronomical Image Denoising with Self-supervised Neural Networks

Abstract

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 − 0.90 + 0.77 % ) and for Gaussian noise when image data has a smooth signal profile (96.45 − 0.96 + 0.80 % ).

Detection of Streaks in Astronomical Images Using Machine Learning

“…Another reason for the potential suitability of CNNs for this application is their exceptional performance in denoising applications [25]. Indeed, CNNs have been applied to the problem of denoising astronomical data so that further analysis can be performed more easily [26]. Several architectures have been used for this purpose, including U-Net [27].…”

“…To evaluate our method, we constructed a data-driven workflow in Python to reproducibly generate a dataset utilizing Pyradon's simulation tool and perform our later evaluation. Training datasets that utilize generated data is a rising practice (e.g., [16,24,26]) and allows development of models even when real-world labeled training data is sparse or unavailable. The principle of domain randomization [28] allows our synthetic data to generalize to realworld data.…”