Results 1 to 10 of about 1,596,426 (191)

Positive Competitive Networks for Sparse Reconstruction [PDF]

Neural Computation, 2023
Abstract We propose and analyze a continuous-time firing-rate neural network, the positive firing-rate competitive network (PFCN), to tackle sparse reconstruction problems with non-negativity constraints. These problems, which involve approximating a given input stimulus from a dictionary using a set of sparse (active) neurons, play a ...
Centorrino, Veronica, Gokhale, Anand, Davydov, Alexander, Russo, Giovanni, Bullo, Francesco   +4 more
openaire   +4 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   +2 more sources

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. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various ...
Garsden, H., Girard, J. N., Starck, J. L., Corbel, S., Tasse, C., Woiselle, A., McKean, J. P., van Amesfoort, A. S., Anderson, J., Avruch, I. M., Beck, R., Bentum, M. J., Best, P., Breitling, F., Broderick, J., Brueggen, M., Butcher, H. R., Ciardi, B., de Gasperin, F., de Geus, E., de Vos, M., Duscha, S., Eisloeffel, J., Engels, D., Falcke, H., Fallows, R. A., Fender, R., Ferrari, C., Frieswijk, W., Garrett, M. A., Griessmeier, J., Gunst, A. W., Hassall, T. E., Heald, G., Hoeft, M., Hoerandel, J., van der Horst, A., Juette, E., Karastergiou, A., Kondratiev, V. I., Kramer, M., Kuniyoshi, M., Kuper, G., Mann, G., Markoff, S., McFadden, R., McKay-Bukowski, D., Mulcahy, D. D., Munk, H., Norden, M. J., Orru, E., Paas, H., Pandey-Pommier, M., Pandey, V. N., Pietka, G., Pizzo, R., Polatidis, A. G., Renting, A., Roettgering, H., Rowlinson, A., Schwarz, Dominik, Sluman, J., Smirnov, O., Stappers, B. W., Steinmetz, M., Stewart, A., Swinbank, J., Tagger, M., Tang, Y., Tasse, C., Thoudam, S., Toribio, C., Vermeulen, R., Vocks, C., van Weeren, R. J., Wijnholds, S. J., Wise, M. W., Wucknitz, O., Yatawatta, S., Zarka, P., Zensus, A.   +80 more
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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

SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse views [PDF]

European Conference on Computer Vision, 2022
We introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images. This task becomes more difficult when only sparse images are provided as input, a scenario where existing neural reconstruction ...
Xiaoxiao Long, Chu-Hsing Lin, Peng Wang, T. Komura, Wenping Wang   +4 more
semanticscholar   +1 more source

Structure-Aware Sparse-View X-Ray 3D Reconstruction [PDF]

Computer Vision and Pattern Recognition, 2023
X-ray, known for its ability to reveal internal structures of objects, is expected to provide richer information for 3D reconstruction than visible light.
Yuanhao Cai, Jiahao Wang, Alan L. Yuille, Zongwei Zhou, Angtian Wang   +4 more
semanticscholar   +1 more source

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

A framelet sparse reconstruction method for pansharpening with guaranteed convergence

Inverse Problems and Imaging, 2023
Pansharpening refers to the super resolution of a low-resolution multispectral (LR-MS) image in virtue of an aligned panchromatic (PAN) image. Such an inverse problem mainly requires a proper use of the spatial information from the auxiliary PAN image ...
Zhong-Cheng Wu, Ting-Zhu Huang, Liang-Jian Deng, G. Vivone   +3 more
semanticscholar   +1 more source

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

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