HyperAIRI: a plug-and-play algorithm for precise hyperspectral image reconstruction in radio interferometry

• URL: http://arxiv.org/abs/2510.15198v1
• Date: Thu, 16 Oct 2025 23:49:20 GMT
• Title: HyperAIRI: a plug-and-play algorithm for precise hyperspectral image reconstruction in radio interferometry
• Authors: Chao Tang, Arwa Dabbech, Adrian Jackson, Yves Wiaux,
• Abstract summary: We introduce HyperAIRI, its hyperspectral extension, underpinned by learned hyperspectral denoisers enforcing a power-law spectral model.<n>For each spectral channel, the HyperAIRI denoiser takes as input its current image estimate, alongside estimates of its two immediate neighbouring channels and the spectral index map.<n>To accommodate varying dynamic ranges, we assemble a shelf of pre-trained denoisers, each tailored to a specific dynamic range.
• Score: 9.387735688431862
• License: http://creativecommons.org/licenses/by/4.0/
• 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 (PnP) approach taking the forward-backward algorithmic structure (FB), has demonstrated state-of-the-art performance in monochromatic RI imaging by alternating a data-fidelity step with a regularisation 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 neighbouring 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 regularisation enforcing non-expansiveness. To accommodate varying dynamic ranges, we assemble a shelf of pre-trained 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 pre-trained 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 optimisation-based algorithm HyperSARA, promoting joint sparsity across spectral channels via the l2,1-norm, also adopting FB. We evaluate HyperAIRI's performance on simulated and real observations. We showcase its superior performance compared to its optimisation-based counterpart Hyper-uSARA, CLEAN's hyperspectral variant in WSClean, and the monochromatic imaging algorithms AIRI and uSARA.

Related papers

• Hyperspectral Adapter for Semantic Segmentation with Vision Foundation Models [18.24287471339871]
  Hyperspectral imaging (HSI) captures spatial information along with dense spectral measurements across numerous narrow wavelength bands.<n>Our architecture incorporates a spectral transformer and a spectrum-aware spatial prior module to extract rich spatial-spectral features.<n>Our architecture achieves state-of-the-art semantic segmentation performance while directly using HSI inputs, outperforming both vision-based and hyperspectral segmentation methods.
  arXiv  Detail & Related papers  (2025-09-24T13:32:07Z)
• Latent Wavelet Diffusion For Ultra-High-Resolution Image Synthesis [56.311477476580926]
  We present Latent Wavelet Diffusion (LWD), a lightweight training framework that significantly improves detail and texture fidelity in ultra-high-resolution (2K-4K) image synthesis.<n>LWD introduces a novel, frequency-aware masking strategy derived from wavelet energy maps, which dynamically focuses the training process on detail-rich regions of the latent space.
  arXiv  Detail & Related papers  (2025-05-31T07:28:32Z)
• Hyperspectral Gaussian Splatting [9.744861764579706]
  3D reconstruction methods have been used to create implicit neural representations of hyperspectral scenes.<n>NeRF is a cutting-edge implicit representation that can render hyperspectral channel compositions of each spatial location from any viewing direction.<n>We propose Hyperspectral Gaussian Splatting (HS-GS) to enable 3D explicit reconstruction of the hyperspectral scenes and novel view synthesis for the entire spectral range.
  arXiv  Detail & Related papers  (2025-05-28T02:07:52Z)
• CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis [69.02751635551724]
  Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding.<n> variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies.<n>We introduce CARL, a model for Camera-Agnostic Representation Learning across RGB, multispectral, and hyperspectral imaging modalities.
  arXiv  Detail & Related papers  (2025-04-27T13:06:40Z)
• A Hybrid Wavelet-Fourier Method for Next-Generation Conditional Diffusion Models [0.0]
  We present a novel generative modeling framework,Wavelet-Fourier-Diffusion, which adapts the diffusion paradigm to hybrid frequency representations.<n>We show how the hybrid frequency-based representation improves control over global coherence and fine texture synthesis.
  arXiv  Detail & Related papers  (2025-04-04T17:11:04Z)
• HyperDet: Generalizable Detection of Synthesized Images by Generating and Merging A Mixture of Hyper LoRAs [17.88153857572688]
  We introduce a novel and generalizable detection framework termed HyperDet. In this work, we propose a novel objective function that balances the pixel and semantic artifacts effectively. Our work paves a new way to establish generalizable domain-specific fake image detectors based on pretrained large vision models.
  arXiv  Detail & Related papers  (2024-10-08T13:43:01Z)
• Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising [54.110544509099526]
  Hyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. We propose a hybrid convolution and attention network (HCANet) to enhance HSI denoising. Experimental results on mainstream HSI datasets demonstrate the rationality and effectiveness of the proposed HCANet.
  arXiv  Detail & Related papers  (2024-03-15T07:18:43Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising [64.11157141177208]
  We propose a spectral enhanced rectangle Transformer to model the spatial and spectral correlation in hyperspectral images. For the former, we exploit the rectangle self-attention horizontally and vertically to capture the non-local similarity in the spatial domain. For the latter, we design a spectral enhancement module that is capable of extracting global underlying low-rank property of spatial-spectral cubes to suppress noise.
  arXiv  Detail & Related papers  (2023-04-03T09:42:13Z)
• Image reconstruction algorithms in radio interferometry: from handcrafted to learned denoisers [7.1439425093981574]
  We introduce a new class of iterative image reconstruction algorithms for radio interferometry, 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. 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.
  arXiv  Detail & Related papers  (2022-02-25T20:26:33Z)
• Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration [66.68541690283068]
  We propose a unified paradigm combining the spatial and spectral properties for hyperspectral image restoration. The proposed paradigm enjoys performance superiority from the non-local spatial denoising and light computation complexity. Experiments on HSI denoising, compressed reconstruction, and inpainting tasks, with both simulated and real datasets, demonstrate its superiority.
  arXiv  Detail & Related papers  (2020-10-24T15:53:56Z)
• Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery [79.69449412334188]
  In this paper, we investigate how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches. We introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images.
  arXiv  Detail & Related papers  (2020-05-18T14:25:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.