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Poster
in
Workshop: Learning-Based Solutions for Inverse Problems

Boosting Weakly Convex Ridge Regularizers with Spatial Adaptivity

Sebastian Neumayer · Mehrsa Pourya · Alexis Goujon · Michael Unser

Keywords: [ Inverse Problems ] [ splines ] [ Denoising ] [ conditional priors ] [ data-driven priors ]


Abstract:

We propose to enhance 1-weakly convex ridge regularizers for image reconstruction by incorporating spatial adaptivity. To this end, we resort to a neural network that generates a weighting mask from an initial reconstruction, which is obtained with the baseline regularizer. Empirically, the learned mask can capture long-range dependencies and leads to a smaller penalization of inherent image structures. Our experiments show that spatial adaptivity improves the performance of image denoising and MRI reconstruction.

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