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• W4375868778 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." @default.
• W4375868778 created "2023-05-10" @default.
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• W4375868778 date "2023-06-04" @default.
• W4375868778 modified "2023-09-27" @default.
• W4375868778 title "Deep Network Series for Large-Scale High-Dynamic Range Imaging" @default.
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• W4375868778 doi "https://doi.org/10.1109/icassp49357.2023.10094843" @default.
• W4375868778 hasPublicationYear "2023" @default.
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