Unrolled Optimization with Deep Priors

  title={Unrolled Optimization with Deep Priors},
  author={Steven Diamond and Vincent Sitzmann and Felix Heide and Gordon Wetzstein},
A broad class of problems at the core of computational imaging, sensing, and low-level computer vision reduces to the inverse problem of extracting latent images that follow a prior distribution, from measurements taken under a known physical image formation model. Traditionally, hand-crafted priors along with iterative optimization methods have been used to solve such problems. In this paper we present unrolled optimization with deep priors, a principled framework for infusing knowledge of the… CONTINUE READING
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Distributed optimization and statistical learning via the alternating direction method of multipliers

  • S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein
  • Foundations and Trends in Machine Learning, 3(1…
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