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

arXiv: https://arxiv.org/abs/2210.16060 · HTML: https://mlsanity.com/paper/2210.16060/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Plug-and-Play Image Restoration with Deep Denoiser Prior](https://mlsanity.com/paper/2008.13751/) (2020)
- [Learned Primal-dual Reconstruction](https://mlsanity.com/paper/1707.06474/) (2017)
- [Plug and play methods for magnetic resonance imaging (long version)](https://mlsanity.com/paper/1903.08616/) (2019)
- [Wide Activation for Efficient and Accurate Image Super-Resolution](https://mlsanity.com/paper/1808.08718/) (2018)
- [Learning Maximally Monotone Operators for Image Recovery](https://mlsanity.com/paper/2012.13247/) (2020)
- [Model based learning for accelerated, limited-view 3D photoacoustic tomography](https://mlsanity.com/paper/1708.09832/) (2017)
- [Convolutional Deep Denoising Autoencoders for Radio Astronomical Images](https://mlsanity.com/paper/2110.08618/) (2021)
- [Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers](https://mlsanity.com/paper/2202.12959/) (2022)
- [First AI for deep super-resolution wide-field imaging in radio astronomy: unveiling structure in ESO 137--006](https://mlsanity.com/paper/2207.11336/) (2022)
- [Data-driven nonsmooth optimization](https://mlsanity.com/paper/1808.00946/) (2018)
- [Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): I. Algorithm and simulations](https://mlsanity.com/paper/2003.07358/) (2020)

## Built on by (most cited first, 4 of 4)

- [Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography](https://mlsanity.com/paper/2304.01963/) (2023)
- [Reconstruct Anything Model: a lightweight general model for computational imaging](https://mlsanity.com/paper/2503.08915/) (2025)
- [Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2](https://mlsanity.com/paper/2403.17905/) (2024)
- [Interlaced R2D2 DNN Series for Scalable Non-Cartesian MRI with Sensitivity Self-calibration](https://mlsanity.com/paper/2503.09559/) (2025)

## Abstract (arXiv)

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.