Deep network series for large-scale high-dynamic range imaging
This addresses scalability and high-dynamic range challenges in computational imaging, particularly for applications like radio astronomy, but is incremental as it builds on existing deep learning and iterative methods.
The paper tackles the problem of large-scale high-dynamic range computational imaging by proposing a residual DNN series approach, which achieves reconstruction quality competitive with Plug-and-Play methods at a fraction of the computational cost, as demonstrated on radio-astronomical imaging simulations.
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.