Independent Component Analysis (ICA) models are very popular semiparametric models in which we observe independent copies of a random vector X =AS, where A is a non-singular matrix and S has independent components. We propose a new way of estimating the unmixing matrix W = A−1 and the marginal distributions of the components of S using nonparametric maximum likelihood. Specifically, we study th...

Abstract: Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing. L1/2 and L2 regularizers can be added to NMF to enforce sparseness and evenness, respectively. In practice, a region in a hyperspectral image may posses...

One of the most important issues in biomedical research microscopy is the analysis of the spatial localization of fluorescently labelled structures. Today, a wide range of available dyes, fluorescent proteins and labelling techniques allows the creation of complex multicolored samples to study intracellular localization. However, analysis of localization and colocalization is often perturbed by...

Multispectral karyotyping analyzes all chromosomes in a single cell by labeling them with chromosome-specific probes conjugated to unique combinations of fluorophores. Currently available multispectral karyotyping systems require the purchase of specialized equipment and reagents. However, conventional laser scanning confocal microscopes that are capable of separating multiple overlapping emiss...

Mixing fluid in a container at low Reynolds number--in an inertialess environment--is not a trivial task. Reciprocating motions merely lead to cycles of mixing and unmixing, so continuous rotation, as used in many technological applications, would appear to be necessary. However, there is another solution: movement of the walls in a cyclical fashion to introduce a geometric phase. We show using...

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays increasingly significant role to solve this problem. In article, we present comprehensive survey the NMF-based methods proposed for unmixing. Taking NMF model as baseline, show how improve by uti...

Hyperspectral images are 3D data sets collected over an x, y grid, where the pixel at each x, y is composed of a spectrum. In hyperspectral unmixing, such a data set X composed of n observed spectra with p wavelengths or spectral bands is decomposed to identify the pure component spectra. Such data sets are found in airborne land imaging studies, biomedical and art history investigations as wel...

Hyperspectral unmixing is recognized as an important tool to learn the constituent materials and corresponding distribution in a scene. The physical spectral mixture model always tackle this problem because of its highly ill-posed nature. In article, we introduce linear (LMM)-based end-to-end deep neural network named SNMF-Net for hyperspectral unmixing. shares alternating architecture benefits...