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