Astronomical image denoising by self-supervised deep learning and restoration processes

TL;DR

This paper introduces a self-supervised deep learning method, TDR, for astronomical image denoising that improves noise reduction and weak signal enhancement, enabling efficient processing of large astronomical datasets.

Contribution

It adapts Self2Self training for astronomical images, incorporating a restoration process to control deviation, and demonstrates its effectiveness on solar magnetograms and Hubble galaxy images.

Findings

Noise level reduced from 8 G to 2 G in solar magnetograms

Weak galaxy structures become clearer after denoising

Method applicable across various disciplines

Abstract

Image denoising based on deep learning has witnessed significant advancements in recent years. However, existing deep learning methods lack quantitative control of the deviation or error on denoised images. The neural networks Self2Self is designed for denoising single-image, training on it and denoising itself, during which training is costly. In this work we explore training Self2Self on an astronomical image and denoising other images of the same kind, which is suitable for quickly denoising massive images in astronomy. To address the deviation issue, the abnormal pixels whose deviation exceeds a predefined threshold are restored to their initial values. The noise reduction includes training, denoising, restoring and named TDR-method, by which the noise level of the solar magnetograms is improved from about 8 G to 2 G. Furthermore, the TDR-method is applied to galaxy images from the Hubble Space Telescope and makes weak galaxy structures become much clearer. This capability of enhancing weak signals makes the TDR-method applicable in various disciplines.