ReviewScientific reports2022
A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging.
Review in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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Who cites it
27 citing papers in PubMed, 141 citations in OpenAlex.
- Seeing Beyond RGB Capabilities: Data-Driven and Physics-Guided Broadband Spectral Extrapolation of Plasmonic Nanostructures by Deep Learning.ACS photonics · 2026Article
- Automated Individual-Level ROI-to-Spectrum Extraction for Hyperspectral Analysis in Forensic Entomology.Insects · 2026Article
- Compact adaptive spectral imager enabled by MEMS Fabry-Perot filtering chip in longwave infrared.Microsystems & nanoengineering · 2026Article
- Inverse Design for Robust Inference in Integrated Computational Spectrometry.Nanophotonics (Berlin, Germany) · 2026Article
- Assessing RGB Color Reliability via Simultaneous Comparison with Hyperspectral Data on PantoneJournal of imaging · 2026Article
- Enhancing Early GI Disease Detection with Spectral Visualization and Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Recent Advancements in Hyperspectral Image Reconstruction from a Compressive Measurement.Sensors (Basel, Switzerland) · 2025Review
- Assessing the Efficacy of the Spectrum-Aided Vision Enhancer (SAVE) to Detect Acral Lentiginous Melanoma, Melanoma In Situ, Nodular Melanoma, and Superficial Spreading Melanoma: Part II.Diagnostics (Basel, Switzerland) · 2025Article
- Trends in Snapshot Spectral Imaging: Systems, Processing, and Quality.Sensors (Basel, Switzerland) · 2025Article
- Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.Foods (Basel, Switzerland) · 2025Review
- Hyperspectral Information Extraction With Full Resolution From Arbitrary Photographs.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- Reconstructed hyperspectral imaging forFrontiers in plant science · 2025Article
- RGB to hyperspectral: Spectral reconstruction for enhanced surgical imaging.Healthcare technology letters · 2024Article
- Colorectal cancer histopathology image analysis: A comparative study of prognostic values of automatically extracted morphometric nuclear features in multispectral and red-blue-green imagery.Histology and histopathology · 2024Article
- Opto-intelligence spectrometer using diffractive neural networks.Nanophotonics (Berlin, Germany) · 2024Article
- Spectral Reconstruction from RGB Imagery: A Potential Option for Infinite Spectral Data?Sensors (Basel, Switzerland) · 2024Article
- RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review.Plants (Basel, Switzerland) · 2024Review
- Editorial: Deep learning approaches applied to spectral images for plant phenotyping.Frontiers in plant science · 2024Article
- Illumination-Based Color Reconstruction for the Dynamic Vision Sensor.Sensors (Basel, Switzerland) · 2023Article
- Towards the synthesis of spectral imaging and machine learning-based approaches for non-invasive phenotyping of plants.Biophysical reviews · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.