Deep-Learning-Driven High-Fidelity In Vivo Hyperspectral Fluorescence Imaging Under Extreme Photon-Limited Conditions. [PDF]
Adv Sci (Weinh)Highly overlapping fluorescent signals become distinguishable in photon‐limited living organisms via advanced imaging with intelligent reconstruction. The resulting in vivo hyperspectral imaging capability reveals nanoplastic uptake and circulation in live zebrafish, providing a new approach for studying complex biological and environmental processes ...
Li R +11 more
europepmc +2 more sources
Phasor-Based Spatio-Spectral Segmentation for Hyperspectral Fluorescence Microscopy. [PDF]
J BiophotonicsSpatially‐corrected density peak clustering (SC‐DPC) enables high‐accuracy segmentation of hyperspectral fluorescence microscopy data in the presence of significant spectral and spatial overlap between fluorophores. Validated on retinal imaging, SC‐DPC effectively separates overlapping fluorophore spectra in photon‐starved conditions, offering superior
Barna M +4 more
europepmc +2 more sources
A Supervised Method for Nonlinear Hyperspectral Unmixing [PDF]
Remote Sensing, 2019 Due to the complex interaction of light with the Earth’s surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occurring in a given resolution cell of hyperspectral data. Our aim is to estimate the fractional abundance maps of the materials from the nonlinear hyperspectral data.
Richard Gloaguen +1 more
exaly +5 more sources
Structured Sparse Method for Hyperspectral Unmixing [PDF]
ISPRS Journal of Photogrammetry and Remote Sensing, 2014 Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data.
Bin Fan, Shiming Xiang, Feiyun Zhu
exaly +3 more sources
Endmember-Free Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingUnmixing networks for hyperspectral images (HSIs) often need to be redesigned for each sensor and initialized with endmember-estimation algorithms, which limits cross-scene generalization.
Baisen Liu +6 more
doaj +2 more sources
Blind and endmember guided autoencoder model for unmixing the absorbance spectra of phytoplankton pigments [PDF]
Scientific ReportsHyperspectral sensing of phytoplankton, free-living microscopic photosynthetic organisms, offers a comprehensive and scalable method for assessing water quality and monitoring changes in aquatic ecosystems.
Pritish Naik +2 more
doaj +2 more sources
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders [PDF]
Proceedings of the National Academy of Sciences of the United States of AmericaDimitar Georgiev +2 more
exaly +2 more sources
Autoencoder-Based Hyperspectral Unmixing with Simultaneous Number-of-Endmembers Estimation [PDF]
SensorsMaher Benismail, Ouiem Bchir
exaly +2 more sources
Robust Hyperspectral Unmixing with Practical Learning-Based Hyperspectral Image Denoising
Remote Sensing, 2023 The noise corruption problem commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral unmixing algorithms.
Risheng Huang +4 more
doaj +1 more source
Robust Dual Spatial Weighted Sparse Unmixing for Remotely Sensed Hyperspectral Imagery
Remote Sensing, 2023 Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries.
Chengzhi Deng +7 more
doaj +1 more source