Uncertainty Quantification in HSI Reconstruction using Physics-Aware Diffusion Priors and Optics-Encoded Measurements

• URL: http://arxiv.org/abs/2511.18473v1
• Date: Sun, 23 Nov 2025 14:43:51 GMT
• Title: Uncertainty Quantification in HSI Reconstruction using Physics-Aware Diffusion Priors and Optics-Encoded Measurements
• Authors: Juan Romero, Qiang Fu, Matteo Ravasi, Wolfgang Heidrich,
• Abstract summary: We formulate hyperspectral image (HSI) reconstruction as a Bayesian inference problem.<n>We propose a framework, HSDiff, that utilizes an unconditionally trained, pixel-level diffusion prior and posterior diffusion sampling.<n>We demonstrate that HSDiff offers a complete, high-performance method for uncertainty-aware HSI reconstruction.
• Score: 12.90606332269204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral image reconstruction from a compressed measurement is a highly ill-posed inverse problem. Current data-driven methods suffer from hallucination due to the lack of spectral diversity in existing hyperspectral image datasets, particularly when they are evaluated for the metamerism phenomenon. In this work, we formulate hyperspectral image (HSI) reconstruction as a Bayesian inference problem and propose a framework, HSDiff, that utilizes an unconditionally trained, pixel-level diffusion prior and posterior diffusion sampling to generate diverse HSI samples consistent with the measurements of various hyperspectral image formation models. We propose an enhanced metameric augmentation technique using region-based metameric black and partition-of-union spectral upsampling to expand training with physically valid metameric spectra, strengthening the prior diversity and improving uncertainty calibration. We utilize HSDiff to investigate how the studied forward models shape the posterior distribution and demonstrate that guiding with effective spectral encoding provides calibrated informative uncertainty compared to non-encoded models. Through the lens of the Bayesian framework, HSDiff offers a complete, high-performance method for uncertainty-aware HSI reconstruction. Our results also reiterate the significance of effective spectral encoding in snapshot hyperspectral imaging.

Related papers

• IRSDE-Despeckle: A Physics-Grounded Diffusion Model for Generalizable Ultrasound Despeckling [0.2580765958706854]
  We present a diffusion-based ultrasound despeckling method built on the Image Restoration Differential Equations framework.<n>To enable supervised training, we curate large paired datasets by simulating ultrasound images from speckle-free magnetic resonance images.<n>The proposed model reconstructs speckle-suppressed images anatomically meaningful edges and contrast.
  arXiv  Detail & Related papers  (2026-02-26T07:42:25Z)
• Spectral Super-Resolution Neural Operator with Atmospheric Radiative Transfer Prior [28.251877082351744]
  Spectral super-resolution (SSR) aims to reconstruct hyperspectral images (HSIs) from multispectral observations.<n>Data-driven methods are widely used, but they often overlook physical principles, leading to unrealistic spectra.<n>We propose the Spectral Super-Resolution Neural Operator (SSRNO), which incorporates atmospheric radiative transfer (ART) prior into the data-driven procedure.
  arXiv  Detail & Related papers  (2025-11-22T02:58:03Z)
• GEWDiff: Geometric Enhanced Wavelet-based Diffusion Model for Hyperspectral Image Super-resolution [19.608052570649303]
  We propose a novel framework for reconstructing hyperspectral images at 4-times super-resolution.<n>A wavelet-based encoder-decoder is introduced that efficiently compresses HSIs into a latent space while preserving spectral-spatial information.<n>Our model demonstrated state-of-the-art results across multiple dimensions, including fidelity, spectral accuracy, visual realism, and clarity.
  arXiv  Detail & Related papers  (2025-11-10T13:44:16Z)
• Hyperspectral Image Generation with Unmixing Guided Diffusion Model [21.078386095749398]
  Hyperspectral image synthesis is a problem that has garnered growing interest yet remains constrained by the conditional generative paradigms that limit sample diversity.<n>We introduce a diffusion framework explicitly guided by hyperspectral unmixing to overcome the challenges.<n>The approach integrates two collaborative components: (i) an unmixing autoencoder that projects generation from the image domain into a low-dimensional abundance manifold, and (ii) an abundance diffusion process that enforces non-negativity and sum-to-one constraints.
  arXiv  Detail & Related papers  (2025-06-03T08:27:10Z)
• 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)
• 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)
• DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model [46.68717345017946]
  Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing. We propose a novel unsupervised HSI-CD with semantic correlation diffusion model (DiffUCD) Our method can achieve comparable results to those fully supervised methods requiring numerous samples.
  arXiv  Detail & Related papers  (2023-05-21T09:21:41Z)
• SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models [18.391049303136715]
  We propose a generative framework for HSI classification with diffusion models (SpectralDiff) SpectralDiff effectively mines the distribution information of high-dimensional and highly redundant data. Experiments on three public HSI datasets demonstrate that the proposed method can achieve better performance than state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-12T16:32:34Z)
• DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration [103.79030498369319]
  Self-supervised diffusion model for hyperspectral image restoration is proposed. textttDDS2M enjoys stronger ability to generalization compared to existing diffusion-based methods. Experiments on HSI denoising, noisy HSI completion and super-resolution on a variety of HSIs demonstrate textttDDS2M's superiority over the existing task-specific state-of-the-arts.
  arXiv  Detail & Related papers  (2023-03-12T14:57:04Z)
• Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction [127.20208645280438]
  Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement. Modeling the inter-spectra interactions is beneficial for HSI reconstruction. Mask-guided Spectral-wise Transformer (MST) proposes a novel framework for HSI reconstruction.
  arXiv  Detail & Related papers  (2021-11-15T16:59:48Z)
• Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
  We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output. We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
  arXiv  Detail & Related papers  (2020-03-15T23:07:56Z)

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.