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In this paper, we study the compressed sensing (CS) image recovery problem. The traditional method divides the image into blocks and treats each block as an independent sub-CS recovery task. This often results in losing global structure of an image. In order to improve the CS recovery result, we propose a nonlocal (NL) estimation step after the initial CS recovery for denoising purpose. The NL estimation...
This paper focuses on detecting activated voxels in fMRI data by exploiting the sparsity of the BOLD signal. Due to the large volume of the data, we propose to learn a dictionary from the compressed measurements of the BOLD signal. The solution to the inverse problem induced by the General Linear Model is then sought through sparse coding using the double sparsity model, where sparsity is imposed...
This paper presents an unobtrusive, energy-efficient approach to human activity sensing through the intelligent scheduling of built-in sensors on mobile phones and light-weight compressed sensing. We refer to this framework as pattern-based compressed phone sensing (P-CPS) where two challenging issues are studied, the energy drainage issue due to continuous sensing which may impede the normal functionality...
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