Rachel O. Schlick

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The recovery of sparse images from noisy, blurry, and potentially low-dimensional observations can be accomplished by solving an optimization problem that minimizes the least-squares error in data fidelity with a sparsity-promoting regularization term (the so-called &#x2113;<sub>2</sub> - &#x2113;<sub>1</sub> minimization problem). This paper focuses on the(More)
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