Kavli Affiliate: Brian Nord
| First 5 Authors: Yunchong Zhang, Brian Nord, Amanda Pagul, Michael Lepori,
| Summary:
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%$}).
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