Boosting Weakly Convex Ridge Regularizers with Spatial Adaptivity

Sebastian Neumayer, Mehrsa Pourya, Alexis Goujon, Michael Unser

Keywords: denoising, inverse problems, data-driven priors, conditional priors, splines

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.

Submission Number: 3