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Compressed sensing and the art of subsampling

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The ability to reconstruct data from under sampled information is highly desirable in many areas, ranging from medical imaging and signal processing to statistics and machine learning. The problem of reconstructing from under sampled data can often be formulated as an underdetermined linear system of equations y = Ax, where the size of y is much smaller than the size of x. Compressed sensing offers a solution to this problem by introducing convex optimisation techniques that under certain assumptions allow for reconstruction of x from under sampled information y. I will give an overview of the concept of compressed sensing and also demonstrate its use in practice and some of its rather intriguing effects especially in signal processing and medical imaging.

This talk is part of the Computer Laboratory Wednesday Seminars series.

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