6 papers
DREAM: Deep-Reparametrization of Adaptive Regularization Maps for Fast Zero-Shot Self-Supervised Learning
Thanh Trung Vu, Ander Biguri, Christoph Kolbitsch +3
Adaptive regularization is an effective means of improving the flexibility of classical variational reconstruction methods while retaining their interpretability and mathematical s…
Learning spatially varying regularisation parameters of low regularity for image reconstruction
Kostas Papafitsoros, Luca Calatroni, Andreas Kofler
In this chapter, we review and discuss the regularity properties of spatially adaptive regularisation weight functions used in variational image reconstruction. Incorporating such…
Box-constrained L0 Bregman-relaxations
Mhamed Essafri, Luca Calatroni, Emmanuel Soubies
Regularization using the L0 pseudo-norm is a common approach to promote sparsity, with widespread applications in machine learning and signal processing. However, solving such prob…
Patch-based learning of adaptive Total Variation parameter maps for blind image denoising
Claudio Fantasia, Luca Calatroni, Xavier Descombes +1
We consider a patch-based learning approach defined in terms of neural networks to estimate spatially adaptive regularisation parameter maps for image denoising with weighted Total…
Learning Spatially Adaptive -Norms Weights for Convolutional Synthesis Regularization
Andreas Kofler, Luca Calatroni, Christoph Kolbitsch +1
We propose an unrolled algorithm approach for learning spatially adaptive parameter maps in the framework of convolutional synthesis-based regularization. More precisely,…
Exact continuous relaxations of l0-regularized criteria with non-quadratic data terms
M'hamed Essafri, Luca Calatroni, Emmanuel Soubies
We propose a new class of exact continuous relaxations of l0-regularized criteria involving non-quadratic data terms such as the Kullback-Leibler divergence and the logistic regres…