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Nonparametric Bayesian approach is considered for learning appropriate dictionaries in sparse image representations. However, for images from multiple separate sources, existing methods have two issues that potentially limit their practical implements: first, learning one unified dictionary is not optimal for representing image samples in different subspaces; second, the required number of dictionaries...
This paper proposes a Bayesian learning approach to structured sparse image reconstruction. In contrast to conventional paradigms which convert images into high-dimensional vectors and thus are impractical for recovering large-scale images, we formulate columns of image matrices into a multiple-measurement-vector (MMV) model to reduce the problem dimension. Besides, we simultaneously exploit the tree...
The known tree-structure of wavelet transform coefficients is considered for conventional sparse image reconstruction to enhance performance. However, although existing Bayesian approaches can learn the latent structures, they have two issues that potentially limit their applications to high resolution images: First, treating the wavelet coefficients of large-scale images as high-dimensional vectors...
The broad class of manifold models are considered for extending the conventional compressive sensing (CS) to a more general framework. However, although the manifold-based CS approaches using a mixture of factor analyzers can learn latent geometric structures of high-dimensional signals, they have two issues that potentially limit their practical use: First, the manifold modeling does not take account...
In the implementation of distributed antenna systems (DASs), the antenna elements in some cases may only be deployed in the form of distributed antenna-clusters (ACs), due to various practical limitations. Consequently, correlation usually exists among the antenna elements within each AC. In contrast to most of the previous work that focused on the antenna correlation in a single-cell environment,...
For the spatially correlated multiuser MIMO-OFDM channels, the conventional iterative MMSE-SIC detection suffers from a considerable performance loss. In this paper, we use the factor graph framework to design robust detection algorithms by clustering a group of symbols to combat the spatial correlation and using the principle of expectation propagation to improve message passing. Furthermore, as...
This paper investigates the linear precoder design that maximizes the average mutual information of multiple-input multiple-output channels with finite-alphabet inputs and statistical channel state information known at the transmitter. This linear precoder design is an important open problem and is extremely difficult to solve: First, average mutual information lacks closed-form expression and involves...
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