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Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture

Amir Aghabiglou, Chung San Chu, Chao Tang, Arwa Dabbech, Yves Wiaux

TL;DR

The paper advances the R2D2 paradigm for radio-interferometric imaging by introducing generalized training that randomizes observation and imaging parameters, a convergence criterion based on data fidelity, and a new U-WDSR architecture to improve reconstruction quality. It demonstrates that these changes yield superior image fidelity, data fidelity, and epistemic-uncertainty handling compared with the earlier R2D2 and with state-of-the-art RI solvers, across synthetic tests and real data. The work also provides an ensemble-based approach to quantify epistemic uncertainty and discusses training cost, showing robust performance with reduced computational overhead due to dynamic data pruning. Overall, these contributions enhance robustness, efficiency, and interpretability of learned RI image formation for large-scale, high-dynamic-range data.

Abstract

The R2D2 Deep Neural Network (DNN) series was recently introduced for image formation in radio interferometry. It can be understood as a learned version of CLEAN, whose minor cycles are substituted with DNNs. We revisit R2D2 on the grounds of series convergence, training methodology, and DNN architecture, improving its robustness in terms of generalizability beyond training conditions, capability to deliver high data fidelity, and epistemic uncertainty. First, while still focusing on telescope-specific training, we enhance the learning process by randomizing Fourier sampling integration times, incorporating multiscan multinoise configurations, and varying imaging settings, including pixel resolution and visibility-weighting scheme. Second, we introduce a convergence criterion whereby the reconstruction process stops when the data residual is compatible with noise, rather than simply using all available DNNs. This not only increases the reconstruction efficiency by reducing its computational cost, but also refines training by pruning out the data/image pairs for which optimal data fidelity is reached before training the next DNN. Third, we substitute R2D2's early U-Net DNN with a novel architecture (U-WDSR) combining U-Net and WDSR, which leverages wide activation, dense skip connections, weight normalization, and low-rank convolution to improve feature reuse and reconstruction precision. As previously, R2D2 was trained for monochromatic intensity imaging with the Very Large Array at fixed $512 \times 512$ image size. Simulations on a wide range of inverse problems and a case study on real data reveal that the new R2D2 model consistently outperforms its earlier version in image reconstruction quality, data fidelity, and epistemic uncertainty.

Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture

TL;DR

The paper advances the R2D2 paradigm for radio-interferometric imaging by introducing generalized training that randomizes observation and imaging parameters, a convergence criterion based on data fidelity, and a new U-WDSR architecture to improve reconstruction quality. It demonstrates that these changes yield superior image fidelity, data fidelity, and epistemic-uncertainty handling compared with the earlier R2D2 and with state-of-the-art RI solvers, across synthetic tests and real data. The work also provides an ensemble-based approach to quantify epistemic uncertainty and discusses training cost, showing robust performance with reduced computational overhead due to dynamic data pruning. Overall, these contributions enhance robustness, efficiency, and interpretability of learned RI image formation for large-scale, high-dynamic-range data.

Abstract

The R2D2 Deep Neural Network (DNN) series was recently introduced for image formation in radio interferometry. It can be understood as a learned version of CLEAN, whose minor cycles are substituted with DNNs. We revisit R2D2 on the grounds of series convergence, training methodology, and DNN architecture, improving its robustness in terms of generalizability beyond training conditions, capability to deliver high data fidelity, and epistemic uncertainty. First, while still focusing on telescope-specific training, we enhance the learning process by randomizing Fourier sampling integration times, incorporating multiscan multinoise configurations, and varying imaging settings, including pixel resolution and visibility-weighting scheme. Second, we introduce a convergence criterion whereby the reconstruction process stops when the data residual is compatible with noise, rather than simply using all available DNNs. This not only increases the reconstruction efficiency by reducing its computational cost, but also refines training by pruning out the data/image pairs for which optimal data fidelity is reached before training the next DNN. Third, we substitute R2D2's early U-Net DNN with a novel architecture (U-WDSR) combining U-Net and WDSR, which leverages wide activation, dense skip connections, weight normalization, and low-rank convolution to improve feature reuse and reconstruction precision. As previously, R2D2 was trained for monochromatic intensity imaging with the Very Large Array at fixed image size. Simulations on a wide range of inverse problems and a case study on real data reveal that the new R2D2 model consistently outperforms its earlier version in image reconstruction quality, data fidelity, and epistemic uncertainty.

Paper Structure

This paper contains 14 sections, 12 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. R2D2 Paradigm
  3. RI data model
  4. Algorithmic structure
  5. R2D2 robustness
  6. Generalized training dataset
  7. Series convergence
  8. U-WDSR DNN architecture
  9. Epistemic uncertainty quantification
  10. Training implementation & computational cost
  11. Simulation and results
  12. Benchmark algorithms & parameter choice
  13. Evaluation metrics
  14. Robust R2D2 vs. early version

Figures (2)

  • Figure 1: R2D2 core DNN architectures. The first row panel (a) illustrates the U-Net model architecture aghabiglou2024r2d2. The second row presents the U-WDSR model: panel (b1) shows the U-WDSR architecture and panel (b2) depicts its WDSR layer. The WDSR residual body (in green boxes) is interlaced with the convolutional layers of the U-Net. WDSR consists of 16 consecutive residual blocks. At each stage, the spatial size of feature maps is indicated at the lower center of each box. The number of channels is indicated at the outer edge of each box.
  • Figure 2: Evolution of the size of the training dataset throughout the iterations of R2D2$_{\mathcal{A}_1,\mathcal{T}_2}$ and R2D2$_{\mathcal{A}_2,\mathcal{T}_2}$, shown as a fraction of the size of the initial training dataset.