HyperAIRI: A Plug-and-play Algorithm for Precise Hyperspectral Image Reconstruction in Radio Interferometry

Abstract The next-generation radio-interferometric (RI) telescopes require imaging algorithms capable of forming high-resolution, high-dynamic-range images from large data volumes spanning wide frequency bands. Recently, AIRI, a plug-and-play approach utilizing the forward–backward (FB) algorithmic structure, has demonstrated state-of-the-art performance in monochromatic RI imaging by alternating a data fidelity step with a regularization step via learned denoisers. In this work, we introduce HyperAIRI, its hyperspectral extension, underpinned by learned hyperspectral denoisers enforcing a power-law spectral model. For each spectral channel, the HyperAIRI denoiser takes as input its current image estimate, alongside estimates of its two immediate neighboring channels and the spectral index map, and provides as output its associated denoised image. To ensure convergence of HyperAIRI, the denoisers are trained with a Jacobian regularization enforcing nonexpansiveness. To accommodate varying dynamic ranges, we assemble a shelf of pretrained denoisers, each tailored to a specific dynamic range. At each HyperAIRI iteration, the spectral channels of the target image cube are updated in parallel using dynamic-range-matched denoisers from the pretrained shelf. The denoisers are also endowed with a spatial image faceting functionality, enabling scalability to varied image sizes. Additionally, we formally introduce Hyper-uSARA, a variant of the optimization-based algorithm HyperSARA, promoting joint sparsity across spectral channels via the ℓ 2,1 norm, also adopting FB. We evaluate HyperAIRI’s performance on simulated and real observations. We showcase its superior performance compared to its optimization-based counterpart Hyper-uSARA, CLEAN’s hyperspectral variant in WSClean, and the monochromatic imaging algorithms AIRI and uSARA. HyperAIRI’s MATLAB implementation is available in the BASPLib ( https://basp-group.github.io/BASPLib/ ) code library.