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

Amir Aghabiglou, Matthieu Terris, William Adrian Jackson, Yves Wiaux

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

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 necessary robustness to variations of the measurement setting, 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, where the reconstructed image is built as 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 simulations for radio-astronomical imaging that a series of only few terms provides a high-dynamic range reconstruction of similar quality to state-of-the-art PnP approaches, at a fraction of the cost.
Original languageEnglish
Publication statusPublished - 5 May 2023
Event2023 IEEE International Conference on Acoustics, Speech and Signal Processing - Rhodes Island, Greece
Duration: 4 Jun 202310 Jun 2023


Conference2023 IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated titleICASSP
CityRhodes Island
Internet address


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