Spectral Compressive Imaging via Chromaticity-Intensity Decomposition

• URL: http://arxiv.org/abs/2509.16690v1
• Date: Sat, 20 Sep 2025 13:37:14 GMT
• Title: Spectral Compressive Imaging via Chromaticity-Intensity Decomposition
• Authors: Xiaodong Wang, Zijun He, Ping Wang, Lishun Wang, Yanan Hu, Xin Yuan,
• Abstract summary: We propose a chromaticity-intensity decomposition framework, which disentangles an HSI into a spatially smooth intensity map and a spectrally variant chromaticity cube.<n>Building on this decomposition, we develop CIDNet, a Chromaticity-Intensity Decomposition unfolding network within a dual-camera CASSI system.
• Score: 15.09393076865179
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In coded aperture snapshot spectral imaging (CASSI), the captured measurement entangles spatial and spectral information, posing a severely ill-posed inverse problem for hyperspectral images (HSIs) reconstruction. Moreover, the captured radiance inherently depends on scene illumination, making it difficult to recover the intrinsic spectral reflectance that remains invariant to lighting conditions. To address these challenges, we propose a chromaticity-intensity decomposition framework, which disentangles an HSI into a spatially smooth intensity map and a spectrally variant chromaticity cube. The chromaticity encodes lighting-invariant reflectance, enriched with high-frequency spatial details and local spectral sparsity. Building on this decomposition, we develop CIDNet, a Chromaticity-Intensity Decomposition unfolding network within a dual-camera CASSI system. CIDNet integrates a hybrid spatial-spectral Transformer tailored to reconstruct fine-grained and sparse spectral chromaticity and a degradation-aware, spatially-adaptive noise estimation module that captures anisotropic noise across iterative stages. Extensive experiments on both synthetic and real-world CASSI datasets demonstrate that our method achieves superior performance in both spectral and chromaticity fidelity. Code and models will be publicly available.

Related papers

• Unifying Color and Lightness Correction with View-Adaptive Curve Adjustment for Robust 3D Novel View Synthesis [73.27997579020233]
  We propose Luminance-GS++, a 3DGS-based framework for robust NVS under diverse illumination conditions.<n>Our method combines a globally view-adaptive lightness adjustment with a local pixel-wise residual refinement for precise color correction.
  arXiv  Detail & Related papers  (2026-02-20T16:20:50Z)
• 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)
• Efficient Hyperspectral Image Reconstruction Using Lightweight Separate Spectral Transformers [26.683800044549105]
  We introduce the Lightweight Separate Spectral Transformer (LSST), an innovative architecture tailored for efficient hyperspectral image reconstruction.<n>Our LSST achieves superior performance while requiring fewer FLOPs and parameters, underscoring its efficiency and effectiveness.
  arXiv  Detail & Related papers  (2026-01-03T04:19:14Z)
• 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 [75.25966323298003]
  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 $textbfCARL$, a model for $textbfC$amera-$textbfA$gnostic $textbfR$esupervised $textbfL$ across RGB, multispectral, and hyperspectral imaging modalities.
  arXiv  Detail & Related papers  (2025-04-27T13:06:40Z)
• Mixed-granularity Implicit Representation for Continuous Hyperspectral Compressive Reconstruction [16.975538181162616]
  This study introduces a novel method using implicit neural representation for continuous hyperspectral image reconstruction.<n>By leveraging implicit neural representations, the MGIR framework enables reconstruction at any desired spatial-spectral resolution.
  arXiv  Detail & Related papers  (2025-03-17T03:37:42Z)
• From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction [9.181668145020895]
  We introduce a pixel-level spectral super-resolution (Pixel-SSR) paradigm that reconstructs hyperspectral images from RGB and point spectra.<n>Despite its advantages, Pixel-SSR presents two key challenges: 1) generalizability to novel scenes lacking point spectra, and 2) effective information extraction to promote reconstruction accuracy.
  arXiv  Detail & Related papers  (2025-03-10T02:23:32Z)
• 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)
• Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution [54.293362972473595]
  Image super-resolution (SR) aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts. Current approaches to address SR tasks are either dedicated to extracting RGB image features or assuming similar degradation patterns. We propose a Contourlet refinement gate framework to restore infrared modal-specific features while preserving spectral distribution fidelity.
  arXiv  Detail & Related papers  (2024-11-19T14:24:03Z)
• 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)
• 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)
• Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from RGB [84.1657998542458]
  It has been proven that the reconstruction accuracy relies heavily on the spectral response of the RGB camera in use. This paper explores the filter-array based color imaging mechanism of existing RGB cameras, and proposes to design the IR-cut filter properly for improved spectral recovery.
  arXiv  Detail & Related papers  (2021-03-26T19:42:21Z)

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.