Compressive Hyperspectral Imaging via Approximate Message Passing
نویسندگان
چکیده
منابع مشابه
Scampi: a robust approximate message-passing framework for compressive imaging
Reconstruction of images from noisy linear measurements is a core problem in image processing, for which convex optimization methods based on total variation (TV) minimization have been the long-standing state-of-the-art. We present an alternative probabilistic reconstruction procedure based on approximate message-passing, Scampi, which operates in the compressive regime, where the inverse imag...
متن کاملHyperspectral image unmixing via bilinear generalized approximate message passing
In hyperspectral unmixing, the objective is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels, into N constituent material spectra (or “endmembers”) with corresponding spatial abundances. In this paper, we propose a novel approach to hyperspectral unmixing (i.e., joint estimation of endmembers and abundances) based on loopy belief propagation. In parti...
متن کاملSketched Clustering via Hybrid Approximate Message Passing
In sketched clustering, the dataset is first sketched down to a vector of modest size, from which the cluster centers are subsequently extracted. The goal is to perform clustering more efficiently than with methods that operate on the full training data, such as k-means++. For the sketching methodology recently proposed by Keriven, Gribonval, et al., which can be interpreted as a random samplin...
متن کاملApproximate Message Passing
In this note, I summarize Sections 5.1 and 5.2 of Arian Maleki’s PhD thesis. 1 Notation We denote scalars by small letters e.g. a, b, c, . . ., vectors by boldface small letters e.g. λ,α,x, . . ., matrices by boldface capital letter e.g. A,B,C, . . ., (subsets of) natural numbers by capital letters e.g. N,M, . . .. We denote i’th element of a vector a by ai and (i, j)’th entry of a matrix A by ...
متن کاملParameterless Optimal Approximate Message Passing
Iterative thresholding algorithms are well-suited for high-dimensional problems in sparse recovery and compressive sensing. The performance of this class of algorithms depends heavily on the tuning of certain threshold parameters. In particular, both the final reconstruction error and the convergence rate of the algorithm crucially rely on how the threshold parameter is set at each step of the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Journal of Selected Topics in Signal Processing
سال: 2016
ISSN: 1932-4553,1941-0484
DOI: 10.1109/jstsp.2015.2500190