Results 101 to 110 of about 112,356 (331)

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

Application of Enhanced Weighted Least Squares with Dark Background Image Fusion for Inhomogeneity Noise Removal in Brain Tumor Hyperspectral Images

Applied Sciences
The inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data.
Jiayue Yan, Chenglong Tao, Yuan Wang, Jian Du, Meijie Qi, Zhoufeng Zhang, Bingliang Hu   +6 more
doaj   +1 more source

Frost filtered scale-invariant feature extraction and multilayer perceptron for hyperspectral image classification [PDF]

, 2020
G. Kalaiarasi, S. Uma Maheswari
openalex   +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

HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis

Remote Sensing
The use of a pretrained image classification model (trained on cats and dogs, for example) as a perceptual loss function for hyperspectral super-resolution and pansharpening tasks is surprisingly effective.
Daniel La’ah Ayuba, Jean-Yves Guillemaut, Belen Marti-Cardona, Oscar Mendez   +3 more
doaj   +1 more source

Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification

Remote Sensing, 2019
Hyperspectral image classification is a challenging and significant domain in the field of remote sensing with numerous applications in agriculture, environmental science, mineralogy, and surveillance.
Yang Zhao, Yuan Yuan, Qi Wang
doaj   +1 more source

An investigation of data compression techniques for hyperspectral core imager data [PDF]

, 2008
We investigate algorithms for tractable analysis of real hyperspectral image data from core samples provided by AngloGold Ashanti. In particular, we investigate feature extraction, non-linear dimension reduction using diffusion maps and wavelet ...
Cawse, Kerry-Anne, Damelin, Steven, du Plessis, Louis, McIntyre, Richard, Michael, Sears, Mitchley, Michael   +5 more
core  

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

Metalens‐Enabled Twisted Chromatic Dispersion

Advanced Physics Research, EarlyView.
We successfully demonstrate visible broadband twisted dispersion metalenses. As a proof of concept, we realized two distinct devices: the conical helical and the spring‐like dispersion‐controlled metalenses. These results rigorously validate the universality of our approach in customizing arbitrary continuous 3D dispersion trajectories, thereby ...
Shiyu Zheng, Boyan Fu, Hong Zhang, Shuming Wang   +3 more
wiley   +1 more source

Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation [PDF]

, 2022
Hyungtae Lee, Heesung Kwon
openalex   +1 more source