  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.
