Publication details
Learning to denoise astronomical images with U-nets
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Periodical |
| Magazine / Source | Monthly Notices of the Royal Astronomical Society |
| MU Faculty or unit | |
| Citation | |
| Web | https://doi.org/10.1093/mnras/staa3567 |
| Doi | http://dx.doi.org/10.1093/mnras/staa3567 |
| Keywords | methods: data analysis; techniques: image processing |
| Description | Y Astronomical images are essential for exploring and understanding the Universe. Optical telescopes capable of deep observations, such as the Hubble Space Telescope (HST), are heavily oversubscribed in the Astronomical Community. Images also often contain additive noise, which makes denoising a mandatory step in post-processing the data before further data analysis. In order to maximize the efficiency and information gain in the post-processing of astronomical imaging, we turn to machine learning. We propose ASTRO U-NET, a convolutional neural network for image denoising and enhancement. For a proof-of-concept, we use HST images from Wide Field Camera 3 instrument UV/visible channel with F555W and F606W filters. Our network is able to produce images with noise characteristics as if they are obtained with twice the exposure time, and with minimum bias or information loss. From these images, we are able to recover 95.9 per cent of stars with an average flux error of 2.26 per cent. Furthermore, the images have, on average, 1.63 times higher signal-to-noise ratio than the input noisy images, equivalent to the stacking of at least three input images, which means a significant reduction in the telescope time needed for future astronomical imaging campaigns. |