Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data

Lustig Michael
Lustig Michael
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Abstract:

Basis pursuit is a compressed sensing optimization in which the l1-norm is minimized subject to model error constraints. Here we use a deep neural network prior instead of l1-regularization. Using known noise statistics, we jointly learn the prior and reconstruct images without access to ground-truth data. During training, we use altern...More

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