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Photon limitations arise in spectral imaging, nuclear medicine, astronomy and night vision. The Poisson distribution used to model this noise has variance equal to its mean so blind application of standard noise removals methods yields significant artifacts. Recently, overcomplete dictionaries combined with sparse learning techniques have become extremely popular in image reconstruction. The aim of...
The ℓ2-ℓ1 compressed sensing minimization problem can be solved efficiently by gradient projection. In imaging applications, the signal of interest corresponds to nonnegative pixel intensities; thus, with additional nonnegativity constraints on the reconstruction, the resulting constrained minimization problem becomes more challenging to solve. In this paper, we propose a gradient projection approach...
This paper describes a new methodology and associated theoretical analysis for rapid and accurate extraction of activation regions from functional MRI data. Most fMRI data analysis methods in use today adopt a hypothesis testing approach, in which the BOLD signals in individual voxels or clusters of voxels are compared to a threshold. In order to obtain statistically meaningful results, the testing...
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