Sparse coding networks, which utilize unsupervised learning to maximize coding efficiency, have successfully reproduced response properties found in primary visual cortex [1]. Ho...
William K. Coulter, Cristopher J. Hillar, Guy Isle...
Abstract--In this paper we address the task of accurately reconstructing a distributed signal through the collection of a small number of samples at a data gathering point using Co...
Riccardo Masiero, Giorgio Quer, Michele Rossi, Mic...
1 minimization can be used to recover sufficiently sparse unknown signals from compressed linear measurements. In fact, exact thresholds on the sparsity, as a function of the ratio...
In this paper we present a new compressed sensing model and reconstruction method for multi-detector signal acquisition. We extend the concept of the famous single-pixel camera to...
Torsten Edeler, Kevin Ohliger, Stephan Hussmann, A...
Compressive sensing is the reconstruction of sparse images or signals from very few samples, by means of solving a tractable optimization problem. In the context of MRI, this can ...