Applying a U-Net VAE denoising step to galaxy images before classification is reported to improve accuracy, reaching 97.45% with a GCNN on Galaxy10 DECaLS, although no direct noisy baseline is presented.
et al, 2011 , The EFIGI catalogue of 4458 nearby galaxies with detailed morphology, Astronomy & Astrophysics, 532, A74
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Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising
Applying a U-Net VAE denoising step to galaxy images before classification is reported to improve accuracy, reaching 97.45% with a GCNN on Galaxy10 DECaLS, although no direct noisy baseline is presented.