Deep Network Series for Large-Scale High-Dynamic Range Imaging

Amir Aghabiglou, Matthieu Terris, Adrian Jackson, Yves Wiaux

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We propose a new approach for large-scale high-dynamic range computational imaging. Deep Neural Networks (DNNs) trained end-to-end can solve linear inverse imaging problems almost instantaneously. While unfolded architectures provide robustness to measurement setting variations, embedding large-scale measurement operators in DNN architectures is impractical. Alternative Plug-and-Play (PnP) approaches, where the denoising DNNs are blind to the measurement setting, have proven effective to address scalability and high-dynamic range challenges, but rely on highly iterative algorithms. We propose a residual DNN series approach, also interpretable as a learned version of matching pursuit, where the reconstructed image is a sum of residual images progressively increasing the dynamic range, and estimated iteratively by DNNs taking the back-projected data residual of the previous iteration as input. We demonstrate on radio-astronomical imaging simulations that a series of only few terms provides a reconstruction quality competitive with PnP, at a fraction of the cost.
Original languageEnglish
Title of host publication2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN (Electronic)9781728163277
Publication statusPublished - 5 May 2023


  • astronomical imaging
  • computational imaging
  • deep neural networks
  • plug-and-play
  • unfolded architectures

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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