Results 71 to 80 of about 423,442 (225)

An adaptive stereo basis method for convolutive blind audio source separation [PDF]

, 2008
NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may ...
Abdallah, Abdallah, Aharon, Amari, Amari, Araki, Bell, Cardoso, Cardoso, Cardoso, Davies, Douglas, Emmanuel Vincent, Hyvärinen, Ikeda, Ikram, Jafari, Jourjine, Knapp, Kurita, Lewicki, Makino, Maria G. Jafari, Mark D. Plumbley, Matsuda, Mike E. Davies, Mitianoudis, Mitianoudis, O’Grady, Parra, Samer A. Abdallah, Saruwatari, Sawada, Schmidt, Smaragdis, Torkkola, Vincent, Vincent, Viste, Yilmaz, Zhang   +40 more
core   +1 more source

Activatable smart contrast agents for photoacoustic imaging

Smart Molecules, EarlyView.
This review highlights recent advances in smart activatable photoacoustic imaging (PAI) contrast agents, which dynamically modulate their optical properties in response to external stimuli or microenvironmental cues. It discusses their molecular design, activation mechanisms, biomedical applications, and future prospects for clinically tailored use ...
Donghyeon Oh, Jihye Lee, Suyoung Park, Juyoung Yoon, Won Jong Kim, Chulhong Kim   +5 more
wiley   +1 more source

Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material
Lei Zhou, Xueni Zhang, Jianbo Wang, Xiao Bai, Lei Tong, Liang Zhang, Jun Zhou, Edwin Hancock   +7 more
doaj   +1 more source

Hyperspectral Images Unmixing Based on Abundance Constrained Multi-Layer KNMF

IEEE Access, 2021
Due to the low spatial resolution of the sensors, the hyperspectral images contain mixed pixels. The purpose of hyperspectral unmixing is to decompose the mixed pixels into a series of endmembers and abundance fractions.
Jing Liu, You Zhang, Yi Liu, Caihong Mu
doaj   +1 more source

Dictionary-based Tensor Canonical Polyadic Decomposition

, 2017
To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary.
Cohen, Jérémy E., Gillis, Nicolas
core   +1 more source

Quantum-inspired computational imaging [PDF]

, 2018
Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect.
Altmann, Yoann, Faccio, Daniele, Goyal, Vivek K., Hero, Alfred O., McLaughlin, Stephen, Padgett, Miles J.   +5 more
core   +2 more sources

Advances in high‐resolution photoacoustic imaging techniques for cellular visualization

VIEW, EarlyView.
Photoacoustic imaging has gained increasing attention for its potential in high‐resolution microscopy, particularly in the context of cellular visualization. This review highlights recent advancements in high‐resolution photoacoustic imaging techniques aimed at visualizing cellular structures, particularly explore key system configurations, imaging ...
Hyunjun Kye, Moon Sung Kang, Dongyoung Jo, Byullee Park, Hee Jeong Jang, Jeesu Kim, Dong‐Wook Han   +6 more
wiley   +1 more source

Improved Collaborative Non-Negative Matrix Factorization and Total Variation for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Hyperspectral unmixing (HSU) is an important technique of remote sensing, which estimates the fractional abundances and the mixing matrix of endmembers in each mixed pixel from the hyperspectral image.
Yuan Yuan, Zihan Zhang, Qi Wang
doaj   +1 more source

Sparse Unmixing using an approximate L0 Regularization [PDF]

Advances in Intelligent Systems Research, 2015
Recently, sparse unmixing focuses on finding an optimal subset of spectral signatures in a large spectral spetral library. In most previous work concerned with the sparse unmixing, the linear mixture model has been widely used to determine and quantify the abundance of materials in mixed piexels(1). In this paper, we propose a new sparse unmxing method
JianPing Xiao, Shengqian Wang, Chengzhi Deng, Tai Gao, Yang Guo   +4 more
openaire   +1 more source

Multilayer Structured NMF for Spectral Unmixing of Hyperspectral Images

, 2015
One of the challenges in hyperspectral data analysis is the presence of mixed pixels. Mixed pixels are the result of low spatial resolution of hyperspectral sensors. Spectral unmixing methods decompose a mixed pixel into a set of endmembers and abundance
Ghassemian, Hassan, Rajabi, Roozbeh
core   +1 more source