4 papers
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…
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…
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…