@inproceedings{
neumayer2023boosting,
title={Boosting Weakly Convex Ridge Regularizers with Spatial Adaptivity},
author={Sebastian Neumayer and Mehrsa Pourya and Alexis Goujon and Michael Unser},
booktitle={NeurIPS 2023 Workshop on Deep Learning and Inverse Problems},
year={2023},
url={https://openreview.net/forum?id=qfWlk4RWOZ}
}
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.
Boosting Weakly Convex Ridge Regularizers with Spatial Adaptivity
Sebastian Neumayer Mehrsa Pourya Alexis Goujon Michael Unser