Results 41 to 50 of about 3,939 (225)

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

HYPERSPECTRAL IMAGE RESOLUTION ENHANCEMENT BASED ON SPECTRAL UNMIXING AND INFORMATION FUSION [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012
Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be
J. Bieniarz, D. Cerra, J. Avbelj, P. Reinartz, R. Müller   +4 more
doaj   +1 more source

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

Catadioptric hyperspectral imaging, an unmixing approach [PDF]

IET Computer Vision, 2020
Hyperspectral imaging systems provide dense spectral information on the scene under investigation by collecting data from a high number of contiguous bands of the electromagnetic spectrum. The low spatial resolutions of these sensors frequently give rise to the mixing problem in remote sensing applications.
Didem Ozisik Baskurt, Yalin Bastanlar, Yasemin Yardimci Cetin   +2 more
openaire   +2 more sources

SMILE: Extraction‐free submicron‐resolution mapping of lipid chain length and unsaturation by stimulated Raman imaging

VIEW, EarlyView.
In this work, we develop submicron‐resolution mapping of intracellular lipid elements (SMILE) as an extraction‐free vibrational spectroscopic imaging platform based on hyperspectral stimulated Raman scattering microscopy with a spectral analysis pipeline for pixel‐resolved lipid profiling.
Yihui Zhou, Xiangjie Huang, Xingxin Liu, Ling Zhang, Jie Huang, Jing Zhao, Yongqing Zhang, Jin Zhang, Yanqing Gong, Delong Zhang, Hyeon Jeong Lee   +10 more
wiley   +1 more source

Adaptive Graph Regularized Multilayer Nonnegative Matrix Factorization for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Hyperspectral unmixing is an important technique for remote sensing image analysis. Among various unmixing techniques, nonnegative matrix factorization (NMF) shows unique advantage in providing a unified solution with well physical interpretation.
Lei Tong, Jun Zhou, Bin Qian, Jing Yu, Chuangbai Xiao   +4 more
doaj   +1 more source

Robust linear unmixing with enhanced constraint of classification for hyperspectral remote sensing imagery

IET Image Processing, 2022
Although hyperspectral data, especially spaceborne images, are rich in spectral information, their spatial resolution is usually low due to the limitation of sensor design and other factors.
Haoyang Yu, Jinxue Chi, Xiaodi Shang, Xueji Shen, Jocelyn Chanussot, Yimin Shi   +5 more
doaj   +1 more source

Simplex‐based model for nanoparticle grain identification in four‐dimensional scanning transmission electron microscopy data

Journal of Microscopy, EarlyView.
Abstract Grain identification in polycrystalline nanoparticles, for example, determining which crystal phases are present at each spatial location, is fundamental to materials characterisation. This is particularly challenging when grains overlap extensively, as commonly occurs in four‐dimensional scanning transmission electron microscopy (4D‐STEM ...
Wei Liu, Roberto dos Reis, Chad A. Mirkin, Vinayak P. Dravid, Wei Chen, Daniel W. Apley   +5 more
wiley   +1 more source

Spatial and temporal scales in plant phenotyping for crop water stress assessment: A review

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Water stress is a major limiting factor for crop productivity worldwide, and its impacts are intensifying due to climate variability and increasing water scarcity. This review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment and supporting precision water ...
Daniel Kingsley Cudjoe, Yazhou Sun, Sanai Li, Wenxuan Guo   +3 more
wiley   +1 more source