Titlebook: Scale Space and Variational Methods in Computer Vision; 9th International Co Luca Calatroni,Marco Donatelli,Matteo Santacesaria Conference

geriatrician

Thomas Dagès,Laurent D. Cohen,Alfred M. Brucksteinlus has been the basis of a variety of powerful methods in the ?eld of mechanics of materials for a long time. Examples range from numerical schemes like the ?nite element method to the determination of effective material properties via homogenization and multiscale approaches. In recent years, howe

DOLT

Quaternary Image Decomposition with Cross-Correlation-Based Multi-parameter Selectionms are efficiently solved by means of the alternating directions method of multipliers. Numerical results show the potentiality of the proposed model for the decomposition of textured images corrupted by several kinds of additive white noises.

敏捷

Theoretical Foundations for Pseudo-Inversion of Nonlinear Operatorsytic expressions are given for the PI of some well-known, non-invertible, nonlinear operators, such as hard- or soft-thresholding and ReLU. Finally, we analyze a neural layer and discuss relations to wavelet thresholding.

opportune

Regularized Material Decomposition for K-edge Separation in Hyperspectral Computed Tomographyconstruction in a single variational model performs better than a more conventional two-stage approach. It is further found that better modelling of noise through use of a weighted least-squares data fidelity improves reconstruction and material separation, as does the use total variation and L1-norm regularization.

纬线

CLAN

Compressive Learning of Deep Regularization for Denoisingmpression operator that can be calculated explicitly for the task of learning a regularizer by DNN. We show that the proposed regularizer is capable of modeling complex regularity prior and can be used for denoising.

脊椎动物

Explicit Diffusion of Gaussian Mixture Model Based Image Priorsg tractable, interpretable, and having only a small number of learnable parameters. As a byproduct, our model can be used for reliable noise estimation, allowing blind denoising of images corrupted by heteroscedastic noise.

generic

On Trainable Multiplicative Noise Removal Modelsf the models in case of denoising of highly corrupted images. We show through numerical experiments that the considered trainable models perform better than the state-of-the-art PDE models in terms of peak-signal-to-noise ratio (PSNR).

暗语

运动性

Conference proceedings 2023took place in Santa Margherita di Pula, Italy, in May 2023. .The 57 papers presented in this volume were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Inverse Problems in Imaging; Machine and Deep Learning in Imaging; Optimization for Imagin