CLEANing Cygnus A deep and fast with R2D2

Arwa Dabbech, Amir Aghabiglou, Chung San Chu, Yves Wiaux*

*Corresponding author for this work

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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. 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.
Original languageEnglish
Article numberL34
JournalAstrophysical Journal Letters
Issue number2
Early online date7 May 2024
Publication statusPublished - 10 May 2024


  • Aperture synthesis
  • Astronomy image processing
  • Computational methods
  • Neural networks
  • Radio galaxies
  • Radio interferometry


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