Radio-Interferometric Image Reconstruction with Denoising Diffusion Restoration Models

TL;DR

This paper introduces a novel radio interferometric image reconstruction method using denoising diffusion models trained on radio galaxy data, achieving high fidelity and outperforming traditional techniques.

Contribution

The authors develop a data-driven prior based on DDPMs for radio sky imaging, enabling physics-agnostic, high-fidelity reconstructions from incomplete Fourier data.

Findings

Reconstructed images show significant quality improvements over CLEAN.

Method is agnostic to specific measurement data and incorporates measurement physics.

High-fidelity reconstructions demonstrated on simulated VLA, EHT, and ALMA data.

Abstract

Reconstructing images of the radio sky from incomplete Fourier information is a key challenge in radio astronomy. In this work, we present a method for radio interferometric image reconstruction using a data-driven prior for the radio sky based on denoising diffusion probabilistic models (DDPMs). We train a DDPM on radio galaxy observations from the VLA FIRST survey, then create simulated VLA, EHT, and ALMA observations of radio galaxies. We use an unsupervised posterior sampling method called Denoising Diffusion Restoration Models (DDRM) to reconstruct the corresponding images using our DDPM as a prior. Our approach is agnostic to the measured radio interferometric data and naturally incorporates the physics of the measurement process. We are able to reconstruct images with very high fidelity and demonstrate a marked improvement over image reconstruction techniques that work on gridded visibilities like CLEAN.