# CLEANing Cygnus A deep and fast with R2D2

arXiv: https://arxiv.org/abs/2309.03291 · HTML: https://mlsanity.com/paper/2309.03291/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers](https://mlsanity.com/paper/2202.12959/) (2022)
- [First AI for deep super-resolution wide-field imaging in radio astronomy: unveiling structure in ESO 137--006](https://mlsanity.com/paper/2207.11336/) (2022)
- [Scalable precision wide-field imaging in radio interferometry: I. uSARA validated on ASKAP data](https://mlsanity.com/paper/2302.14148/) (2023)
- [Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): I. Algorithm and simulations](https://mlsanity.com/paper/2003.07358/) (2020)
- [Scalable precision wide-field imaging in radio interferometry: II. AIRI validated on ASKAP data](https://mlsanity.com/paper/2302.14149/) (2023)

## Built on by (most cited first, 4 of 4)

- [The R2D2 deep neural network series paradigm for fast precision imaging in radio astronomy](https://mlsanity.com/paper/2403.05452/) (2024)
- [Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture](https://mlsanity.com/paper/2503.02554/) (2025)
- [Generative imaging for radio interferometry with fast uncertainty quantification](https://mlsanity.com/paper/2507.21270/) (2025)
- [HyperAIRI: a plug-and-play algorithm for precise hyperspectral image reconstruction in radio interferometry](https://mlsanity.com/paper/2510.15198/) (2025)

## Abstract (arXiv)

A novel deep learning paradigm for synthesis imaging by radio interferometry in astronomy was recently proposed, dubbed "Residual-to-Residual DNN series for high-Dynamic range imaging" (R2D2). In this work, we start by shedding light on R2D2's algorithmic structure, interpreting it as a learned version of CLEAN with minor cycles substituted with a deep neural network (DNN) whose training is iteration-specific. We then proceed with R2D2's first demonstration on real data, for monochromatic intensity imaging of the radio galaxy Cygnus A from S band observations with the Very Large Array (VLA). We show that the modeling power of R2D2's learning approach enables delivering high-precision imaging, superseding the resolution of CLEAN, and matching the precision of modern optimization and plug-and-play algorithms, respectively uSARA and AIRI. Requiring few major-cycle iterations only, R2D2 provides a much faster reconstruction than uSARA and AIRI, known to be highly iterative, and is at least as fast as CLEAN.