Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers

TL;DR

This paper presents AIRI, a novel iterative radio interferometry image reconstruction method combining deep learning denoisers with optimization, achieving high-quality images faster than traditional algorithms and outperforming direct DNN approaches.

Contribution

AIRI introduces a new framework integrating learned denoisers into optimization for radio interferometry, enhancing image quality and speed over existing methods.

Findings

AIRI achieves competitive image quality with SARA and uSARA.

AIRI significantly accelerates the reconstruction process.

CLEAN is faster but produces lower quality images.

Abstract

We introduce a new class of iterative image reconstruction algorithms for radio interferometry, at the interface of convex optimization and deep learning, inspired by plug-and-play methods. The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal regularization operator of an optimization algorithm. The proposed AIRI (``AI for Regularization in radio-interferometric Imaging'') framework, for imaging complex intensity structure with diffuse and faint emission from visibility data, inherits the robustness and interpretability of optimization, and the learning power and speed of networks. Our approach relies on three steps. Firstly, we design a low dynamic range training database from optical intensity images. Secondly, we train a DNN denoiser at a noise level inferred from the signal-to-noise ratio of the data. We use training losses enhanced with a nonexpansiveness term ensuring algorithm convergence, and including on-the-fly database dynamic range enhancement via exponentiation. Thirdly, we plug the learned denoiser into the forward-backward optimization algorithm, resulting in a simple iterative structure alternating a denoising step with a gradient-descent data-fidelity step. We have validated AIRI against CLEAN, optimization algorithms of the SARA family, and a DNN trained to reconstruct the image directly from visibility data. Simulation results show that AIRI is competitive in imaging quality with SARA and its unconstrained forward-backward-based version uSARA, while providing significant acceleration. CLEAN remains faster but offers lower quality. The end-to-end DNN offers further acceleration, but with far lower quality than AIRI.