Learning Spatial-Spectral Refinement and Calibrating Complementary Observations for Hyperspectral Image Super-Resolution

• URL: http://arxiv.org/abs/2609.05303v1
• Date: Fri, 04 Sep 2026 15:56:10 GMT
• Title: Learning Spatial-Spectral Refinement and Calibrating Complementary Observations for Hyperspectral Image Super-Resolution
• Abstract summary: We propose Two-Stage Reconstruction with Implicit Neural Representation (TSR-ITNR)<n>TSR-ITNR learns an implicit Tucker representation to refine its low-rank coefficient and spectral basis to better capture fine structures and interband correlations.<n>Experiments on multiple benchmark datasets demonstrate strong quantitative, visual, and spectral reconstruction performance without ground-truth supervision.
• Score: 10.566046888869828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral and multispectral image fusion (HMIF) aims to reconstruct a high-resolution hyperspectral image (HR-HSI) by combining the fine spatial details of a high-resolution multispectral image (HR-MSI) with the rich spectral information of a low-resolution hyperspectral image (LR-HSI). Recent advances in implicit neural representations (INRs) have enabled flexible coordinate-based modeling for HMIF; however, existing INR-based approaches may not fully capture fine-grained spatial structures and rich spectral dependencies. Moreover, the LR-HSI and HR-MSI are primarily incorporated through degradation-consistency constraints, leaving their complementary information underexploited. To address these limitations, we propose Two-Stage Reconstruction with Implicit Tensor Neural Representation (TSR-ITNR), a unified self-supervised framework integrating representation refinement and observation-guided calibration. In Stage 1, TSR-ITNR learns an implicit Tucker representation and refines its low-rank spatial coefficient tensor and spectral basis to better capture fine spatial structures and interband correlations. A fixed pretrained denoiser further provides a deep prior for the preliminary reconstruction. In Stage 2, parameter-free calibration derives complementary and noninterfering corrections from both observations to recover information insufficiently captured in Stage 1. Theoretical analysis establishes the geometry-preserving property of spectral refinement and the orthogonal complementarity of calibration. Extensive experiments on multiple benchmark datasets demonstrate strong quantitative, visual, and spectral reconstruction performance without ground-truth HR-HSI supervision. Beyond conventional reconstruction metrics, we further assess the effectiveness of TSR-ITNR using downstream semantic segmentation accuracy.

Related papers

• Lightweight Interpretable RGB-Guided Hyperspectral Super-Resolution under Real Cross-resolution Misalignment [6.5685272845309255]
  RGB-guided hyperspectral super-resolution transfers spatial detail from a high-resolution RGB guide to a low-resolution hyperspectral image.<n>We propose a lightweight and interpretable RGB-guided HSR framework combining cross-modal flow alignment with model-based Gram-Schmidtization fusion.
  arXiv  Detail & Related papers  (2026-09-01T10:55:11Z)
• HIR-ALIGN: Enhancing Hyperspectral Image Restoration via Diffusion-Based Data Generation [50.46910397782266]
  Hyperspectral image (HSI) restoration is crucial for reliable analysis, as real HSIs suffer from degradations like noise, blur, and resolution loss.<n>We present HIR-ALIGN, a plug-and-play target-adaptive augmentation framework that enhances hyperspectral image restoration by augmenting limited training images with synthetic data that closely matches the target distribution using no extra data.
  arXiv  Detail & Related papers  (2026-05-13T14:14:13Z)
• DegBins: Degradation-Driven Binning for Depth Super-Resolution [57.61783461543096]
  DegBins is a novel DSR framework that leverages degradation-driven binning to adaptively enhance residual modeling.<n>DegBins consistently outperforms existing state-of-the-art methods in terms of accuracy, robustness, and generalization.
  arXiv  Detail & Related papers  (2026-05-10T16:17:11Z)
• ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion [42.573141390377415]
  We propose textbfASSR-Net: an Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion.<n>ASSR-Net consistently outperforms state-of-the-art methods, achieving superior spatial detail preservation and spectral consistency.
  arXiv  Detail & Related papers  (2026-04-07T11:44:04Z)
• HSSDCT: Factorized Spatial-Spectral Correlation for Hyperspectral Image Fusion [11.994592153994482]
  Hyperspectral image (HSI) fusion aims to reconstruct a high-resolution HSI (HR-HSI) by combining the rich spectral information of a low-resolution HSI with the fine details of a high-resolution multispectral image (HR-MSI)<n>Recent deep learning methods have achieved notable progress, but they still suffer from limited receptive fields, redundant spectral bands, and the quadratic complexity of self-attention.<n>We propose the Hierarchical Spatial-Spectral Dense Correlation Network (HSSDCT) to overcome these challenges.
  arXiv  Detail & Related papers  (2026-01-31T03:24:03Z)
• SR$^{2}$-Net: A General Plug-and-Play Model for Spectral Refinement in Hyperspectral Image Super-Resolution [3.4888894498274747]
  HSI-SR aims to enhance spatial resolution while preserving spectrally faithful and physically plausible characteristics.<n>These methods often neglect spectral consistency across bands, leading to spurious oscillations and physically implausible artifacts.<n>We propose a lightweight plug-and-play, physically priors Spectral Rectification Super-Resolution Network (SR$2$-Net) to address this issue.
  arXiv  Detail & Related papers  (2026-01-29T07:00:00Z)
• Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images [64.80875911446937]
  We propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images.<n>For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module.<n>For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module.
  arXiv  Detail & Related papers  (2025-01-02T15:14:40Z)
• 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)
• 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)
• Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning [62.52242684874278]
  Cross-modality distribution of spatial and spectral information makes the problem challenging. We propose a novel textitlightweight deep neural network-based framework, namely PZRes-Net. Our framework learns a high resolution and textitzero-centric residual image, which contains high-frequency spatial details of the scene.
  arXiv  Detail & Related papers  (2020-06-18T06:32:11Z)
• 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)
• Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization [40.146997900687374]
  Hyperspectral super-resolution (HSR) fuses a low-resolution hyperspectral image (HSI) and a high-resolution multispectral image (MSI) to obtain a high-resolution HSI (HR-HSI) We propose a new model, named coupled tensor ring factorization (CTRF), for HSR. The proposed CTRF approach simultaneously learns high spectral resolution core tensor from the HSI and high spatial resolution core tensors from the MSI, and reconstructs the HR-HSI via tensor ring (TR) representation.
  arXiv  Detail & Related papers  (2020-01-06T13:19:59Z)

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.