mlsanityData to 7 Oct 2026

A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging

arXiv 2106.15944 ↗First posted 30 Jun 2021eess.IV cs.CVcited by 20

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Abstract (arXiv)

Hyperspectral imaging enables versatile applications due to its competence in capturing abundant spatial and spectral information, which are crucial for identifying substances. However, the devices for acquiring hyperspectral images are expensive and complicated. Therefore, many alternative spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from lower-cost, more available RGB images. We present a thorough investigation of these state-of-the-art spectral reconstruction methods from the widespread RGB images. A systematic study and comparison of more than 25 methods has revealed that most of the data-driven deep learning methods are superior to prior-based methods in terms of reconstruction accuracy and quality despite lower speeds. This comprehensive review can serve as a fruitful reference source for peer researchers, thus further inspiring future development directions in related domains.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 38 of its 104 references that are in the map. First posted on arXiv 30 Jun 2021.

Builds on

38 of 104 references.

References · 38

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 6; 2025: 8; 2026: 2.

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  1. 2024Limitations of Data-Driven Spectral Reconstruction -- An Optics-Aware Analysiscited by 5
  2. 2025Hyperspectral vs. RGB for Pedestrian Segmentation in Urban Driving Scenes: A Comparative Studycited by 4
  3. 2024Deep learning-based hyperspectral image reconstruction for quality assessment of agro-productcited by 3
  4. 2023RDFNet: Regional Dynamic FISTA-Net for Spectral Snapshot Compressive Imagingcited by 2
  5. 2022Illumination-Based Color Reconstruction for the Dynamic Vision Sensorcited by 1
  6. 2023mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamicscited by 1
  7. 2024Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applicationscited by 1
  8. 2024RGB to Hyperspectral: Spectral Reconstruction for Enhanced Surgical Imagingcited by 1
  9. 2025AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolutioncited by 1
  10. 2025HyperKD: Distilling Cross-Spectral Knowledge in Masked Autoencoders via Inverse Domain Shift with Spatial-Aware Masking and Specialized Losscited by 1
  11. 2023Learning Exhaustive Correlation for Spectral Super-Resolution: Where Spatial-Spectral Attention Meets Linear Dependencecited by 0
  12. 2024Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methodscited by 0
8 more
  1. 2024Hyperspectral Image Reconstruction for Predicting Chick Embryo Mortality Towards Advancing Egg and Hatchery Industrycited by 0
  2. 2025Generative Model-Assisted Demosaicing for Cross-multispectral Camerascited by 0
  3. 2025Hyperspectral Image Generation with Unmixing Guided Diffusion Modelcited by 0
  4. 2025MCGA: Mixture of Codebooks Hyperspectral Reconstruction via Grayscale-Aware Attentioncited by 0
  5. 2025RGB Pre-Training Enhanced Unobservable Feature Latent Diffusion Model for Spectral Reconstructioncited by 0
  6. 2025Uncertainty Quantification in HSI Reconstruction using Physics-Aware Diffusion Priors and Optics-Encoded Measurementscited by 0
  7. 2026Sensor-Adaptive Infrared Spectral Reconstruction with Plug-and-Play Diffusion Priorscited by 0
  8. 2026Compact Low-Cost Hyperspectral Imaging via Angular-to-Spectral Diversity Conversioncited by 0