Deep network series for large-scale high-dynamic range imaging

• URL: http://arxiv.org/abs/2210.16060v3
• Date: Wed, 27 Sep 2023 15:05:19 GMT
• Title: Deep network series for large-scale high-dynamic range imaging
• Authors: Amir Aghabiglou, Matthieu Terris, Adrian Jackson, Yves Wiaux
• Abstract summary: 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. Alternative Plug-and-Play approaches have proven effective to address high-dynamic range challenges, but rely on highly iterative algorithms.
• Score: 2.3759432635713895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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