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Plug-and-play (PnP) methods have recently emerged as a powerful
framework for image reconstruction that can flexibly combine different
physics-based observation models with data-driven image priors
in the form of denoisers, and achieve state-of-the-art image reconstruction
quality in many applications. In this paper, we aim to further
improve the computational efficacy of PnP methods by designing
a new algorithm that makes use of stochastic variance-reduced
gradients (SVRG), a nascent idea to accelerate runtime in stochastic

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In several applications, including imaging of deformable objects while in motion, simultaneous localization and mapping, and unlabeled sensing, we encounter the problem of recovering a signal that is measured subject to unknown permutations. In this paper we take a fresh look at this problem through the lens of optimal transport (OT). In particular, we recognize that in most practical applications the unknown permutations are not arbitrary but some are more likely to occur than others.

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While neural networks have achieved vastly enhanced performance over traditional iterative methods in many cases, they are generally empirically designed and the underlying structures are difficult to interpret. The algorithm unrolling approach has helped connect iterative algorithms to neural network architectures. However, such connections have not been made yet for blind image deblurring. In this paper, we propose a neural network architecture that advances this idea.

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