# Convolutional Deep Denoising Autoencoders for Radio Astronomical Images

arXiv: https://arxiv.org/abs/2110.08618 · HTML: https://mlsanity.com/paper/2110.08618/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 10 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Built on by (most cited first, 10 of 10)

- [DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification](https://mlsanity.com/paper/2112.14299/) (2021)
- [Deep network series for large-scale high-dynamic range imaging](https://mlsanity.com/paper/2210.16060/) (2022)
- [Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds](https://mlsanity.com/paper/2304.01670/) (2023)
- [Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model](https://mlsanity.com/paper/2402.10204/) (2024)
- [Learned Interferometric Imaging for the SPIDER Instrument](https://mlsanity.com/paper/2301.10260/) (2023)
- [A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm](https://mlsanity.com/paper/2302.13854/) (2023)
- [A Generative Model for Disentangling Galaxy Photometric Parameters](https://mlsanity.com/paper/2507.15898/) (2025)
- [On the Shape of Latent Variables in a Denoising VAE-MoG: A Posterior Sampling-Based Study](https://mlsanity.com/paper/2509.25382/) (2025)
- [Reducci'on de ruido por medio de autoencoders: caso de estudio con la se~nal GW150914](https://mlsanity.com/paper/2510.00873/) (2025)
- [HyperAIRI: a plug-and-play algorithm for precise hyperspectral image reconstruction in radio interferometry](https://mlsanity.com/paper/2510.15198/) (2025)

## Abstract (arXiv)

We apply a Machine Learning technique known as Convolutional Denoising Autoencoder to denoise synthetic images of state-of-the-art radio telescopes, with the goal of detecting the faint, diffused radio sources predicted to characterise the radio cosmic web. In our application, denoising is intended to address both the reduction of random instrumental noise and the minimisation of additional spurious artefacts like the sidelobes, resulting from the aperture synthesis technique. The effectiveness and the accuracy of the method are analysed for different kinds of corrupted input images, together with its computational performance. Specific attention has been devoted to create realistic mock observations for the training, exploiting the outcomes of cosmological numerical simulations, to generate images corresponding to LOFAR HBA 8 hours observations at 150 MHz. Our autoencoder can effectively denoise complex images identifying and extracting faint objects at the limits of the instrumental sensitivity. The method can efficiently scale on large datasets, exploiting high performance computing solutions, in a fully automated way (i.e. no human supervision is required after training). It can accurately perform image segmentation, identifying low brightness outskirts of diffused sources, proving to be a viable solution for detecting challenging extended objects hidden in noisy radio observations.