Results 11 to 20 of about 247,748 (272)

LOFAR Sparse Image Reconstruction [PDF]

Astronomy & Astrophysics, 2015
Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness.
Anderson, J., Avruch, I., Beck, R., Bentum, M., Best, P., Breitling, F., Broderick, J., Brüggen, M., Butcher, H., Ciardi, B., Corbel, S., de Gasperin, F., de Geus, E., de Vos, M., Duscha, S., Eislöffel, J., el, J., Engels, D., ey, V., ey-Pommier, M., Falcke, H., Fallows, R., Fender, R., Ferrari, C., Frieswijk, W., Garrett, M., Garsden, H., Girard, J., Grießmeier, J., Gunst, A., Hassall, T., Heald, G., Hoeft, M., Hö,, Juette, E., Karastergiou, A., Kondratiev, V., Kramer, M., Kuniyoshi, M., Kuper, G., Mann, G., Markoff, S., McFadden, R., McKay-Bukowski, D., McKean, J., Mulcahy, D., Munk, H., Norden, M., Orru, E., Paas, H., Pietka, G., Pizzo, R., Polatidis, A., Renting, A., Rowlinson, A., Röttgering, H., Schwarz, D., Sluman, J., Smirnov, O., Stappers, B., Starck, J., Steinmetz, M., Stewart, A., Swinbank, J., Tagger, M., Tang, Y., Tasse, C., Tasse, C., Thoudam, S., Toribio, C., van Amesfoort, A., van der Horst, A., van Weeren, R., Vermeulen, R., Vocks, C., Wijnholds, S., Wise, M., Woiselle, A., Wucknitz, O., Yatawatta, S., Zarka, P., Zensus, A.   +81 more
core   +19 more sources

Sparse image reconstruction for molecular imaging [PDF]

IEEE Transactions on Image Processing, 2008
The application that motivates this paper is molecular imaging at the atomic level. When discretized at sub-atomic distances, the volume is inherently sparse.
Hero III, Alfred O., Raich, Raviv, Ting, Michael   +2 more
core   +3 more sources

Neural 3D reconstruction from sparse views using geometric priors

Computational Visual Media, 2023
Sparse view 3D reconstruction has attracted increasing attention with the development of neural implicit 3D representation. Existing methods usually only make use of 2D views, requiring a dense set of input views for accurate 3D reconstruction.
Tai-Jiang Mu, Hao-Xiang Chen, Jun-Xiong Cai, Ning Guo   +3 more
doaj   +2 more sources

Structure-Based Bayesian Sparse Reconstruction [PDF]

IEEE Transactions on Signal Processing, 2012
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information ...
Al-Naffouri, Tareq Y., Quadeer, Ahmed A.
core   +4 more sources

Sparse Shape Reconstruction [PDF]

SIAM Journal on Imaging Sciences, 2013
This paper introduces a new shape-based image reconstruction technique applicable to a large class of imaging problems formulated in a variational sense. Given a collection of shape priors (a shape dictionary), we define our problem as choosing the right elements and geometrically composing them through basic set operations to characterize desired ...
Aghasi, Alireza, Romberg, Justin
openaire   +2 more sources

Sparse Image Reconstruction using Sparse Priors [PDF]

2006 International Conference on Image Processing, 2006
Sparse image reconstruction is of interest in the fields of radioastronomy and molecular imaging. The observation is assumed to be a linear transformation of the image, and corrupted by additive white Gaussian noise. We study the usage of sparse priors in the empirical Bayes framework: it permits the selection of the hyperparameters of the prior in a ...
Michael Ting, Raviv Raich, Alfred O. Hero   +2 more
openaire   +1 more source

Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images. [PDF]

IEEE Trans Med Imaging, 2017
Fang L, Li S, Cunefare D, Farsiu S.
europepmc   +2 more sources

Sparse poisson intensity reconstruction algorithms [PDF]

2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 2009
The observations in many applications consist of counts of discrete events, such as photons hitting a dector, which cannot be effectively modeled using an additive bounded or Gaussian noise model, and instead require a Poisson noise model. As a result, accurate reconstruction of a spatially or temporally distributed phenomenon (f) from Poisson data (y)
Harmany, Zachary T., Marcia, Roummel F., Willett, Rebecca M.   +2 more
openaire   +2 more sources

Sparse-View Ct Reconstruction Via Convolutional Sparse Coding [PDF]

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019
Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features. To deal with these problems, the convolutional sparse coding (CSC) has been proposed and introduced into various applications.
Bao, Peng, Xia, Wenjun, Yang, Kang, Zhou, Jiliu, Zhang, Yi   +4 more
openaire   +2 more sources

Sparse Reconstruction by Separable Approximation [PDF]

IEEE Transactions on Signal Processing, 2008
Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing (CS) are a few well-known areas in which problems of ...
Stephen J. Wright, Robert D. Nowak, Mario A. T. Figueiredo   +2 more
openaire   +1 more source