Phy-CoSF: Physics-Guided Continuous Spectral Fields Reconstruction and Super-Resolution for Snapshot Compressive Imaging

• URL: http://arxiv.org/abs/2605.13583v1
• Date: Wed, 13 May 2026 14:17:02 GMT
• Title: Phy-CoSF: Physics-Guided Continuous Spectral Fields Reconstruction and Super-Resolution for Snapshot Compressive Imaging
• Abstract summary: We propose Phy-CoSF, which synergizes deep unfolding networks with implicit neural representations.<n>Specifically, we propose a two-phase architecture that bridges discrete-wavelength training with continuous spectral rendering.
• Score: 79.35520607451187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances have demonstrated that coded aperture snapshot spectral imaging (CASSI) systems show great potential for capturing 3D hyperspectral images (HSIs) from a single 2D measurement. Despite the inherent spectral continuity of scenes captured by CASSI, most existing reconstruction methods are restricted to fixed, discrete spectral outputs, thereby precluding continuous spectral reconstruction or spectral super-resolution. To address this challenge, we propose Phy-CoSF, which synergizes deep unfolding networks with implicit neural representations, establishing a new paradigm for continuous spectral reconstruction and super-resolution in CASSI. Specifically, we propose a two-phase architecture that bridges discrete-wavelength training with continuous spectral rendering, enabling the synthesis of high-fidelity HSIs at arbitrary target wavelengths. At the core of our framework lies the continuous spectral fields (CoSF) module, embedded within each unfolding stage as a dynamic prior, which comprises a triple-branch cross-domain feature mixer for comprehensive spatial-frequency-channel feature fusion, alongside a spectral synthesis head that generates spectral intensities by querying continuous wavelength coordinates. Extensive experimental results demonstrate that Phy-CoSF not only achieves continuous modeling at arbitrary spectral resolutions but also outperforms many state-of-the-art methods in both reconstruction fidelity and spectral detail preservation. Our code and more results are available at: https://github.com/PaiDii/Phy-CoSF.git.

Related papers

• FSCM: Frequency-Enhanced Spatial-Spectral Coupled Mamba for Infrared Hyperspectral Image Colorization [10.509841173943387]
  This paper presents a spectral-information-guided GAN framework for infrared colorization.<n>Within FSCM, a frequency-enhanced spatial-spectral state-space generator composed of cascaded FSB units is constructed.<n>Experiments show that FSCM outperforms existing infrared colorization methods in visual quality and semantic fidelity.
  arXiv  Detail & Related papers  (2026-05-13T15:48:58Z)
• FSP-Diff: Full-Spectrum Prior-Enhanced DualDomain Latent Diffusion for Ultra-Low-Dose Spectral CT Reconstruction [20.226435074884808]
  FSP-Diff is a full-spectrum prior-enhanced dual-domain latent diffusion framework for ultra-low-dose spectral CT reconstruction.<n>Our framework integrates direct image reconstructions with projection-domain denoised results.<n>Experiments on simulated and real-world datasets demonstrate that FSP-Diff significantly outperforms state-of-the-art methods in both image quality and computational efficiency.
  arXiv  Detail & Related papers  (2026-02-08T14:13:19Z)
• 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)
• S2ML: Spatio-Spectral Mutual Learning for Depth Completion [56.26679539288063]
  Raw depth images captured by RGB-D cameras often suffer from incomplete depth values due to weak reflections, boundary shadows, and artifacts.<n>Existing methods address this problem through depth completion in the image domain, but they overlook the physical characteristics of raw depth images.<n>We propose a Spatio-Spectral Mutual Learning framework (S2ML) to harmonize the advantages of both spatial and frequency domains for depth completion.
  arXiv  Detail & Related papers  (2025-11-08T15:01:55Z)
• Hybrid Deep Learning for Hyperspectral Single Image Super-Resolution [9.467214671383875]
  We introduce Spectral-Spatial Unmixing Fusion (SSUF), a novel module that can be seamlessly integrated into standard 2D convolutional architectures.<n>The SSUF combines spectral unmixing with spectral--spatial feature extraction and guides a ResNet-based convolutional neural network for improved reconstruction.<n> Experiments on three public remote sensing hyperspectral datasets demonstrate that the proposed hybrid deep learning model achieves competitive performance.
  arXiv  Detail & Related papers  (2025-09-26T08:28:07Z)
• 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)
• PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification [49.37555541088792]
  Phase-Amplitude Decoupling (PAD) is a frequency-aware framework that separates phase (modality-shared) and amplitude (modality-complementary) components.<n>This work establishes a new paradigm for physics-aware multi-modal fusion in remote sensing.
  arXiv  Detail & Related papers  (2025-04-27T07:21:42Z)
• 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)
• Spectral-wise Implicit Neural Representation for Hyperspectral Image Reconstruction [13.79887292039637]
  Coded Aperture Snapshot Spectral Imaging (CASSI) reconstruction aims to recover the 3D spatial-spectral signal from 2D measurement.<n>Existing methods for reconstructing HSI typically involve learning mappings from a 2D compressed image to a predetermined set of discrete spectral bands.<n>We propose an innovative method called Spectral-wise Implicit Neural Representation (SINR) as a pioneering step toward addressing this limitation.
  arXiv  Detail & Related papers  (2023-12-02T08:06:07Z)
• SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution [73.46167948298041]
  We propose a neural implicit model that represents an image as a function of both continuous pixel coordinates in the spatial domain and continuous wavelengths in the spectral domain. We show that SSIF generalizes well to both unseen spatial resolutions and spectral resolutions. It can generate high-resolution images that improve the performance of downstream tasks by 1.7%-7%.
  arXiv  Detail & Related papers  (2023-09-30T15:23:30Z)

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.