2023
DOI: 10.48550/arxiv.2303.05386
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Deep Equilibrium Learning of Explicit Regularizers for Imaging Inverse Problems

Abstract: There has been significant recent interest in the use of deep learning for regularizing imaging inverse problems. Most work in the area has focused on regularization imposed implicitly by convolutional neural networks (CNNs) pre-trained for image reconstruction. In this work, we follow an alternative line of work based on learning explicit regularization functionals that promote preferred solutions. We develop the Explicit Learned Deep Equilibrium Regularizer (ELDER) method for learning explicit regularizers t… Show more

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References 34 publications
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