Python's tours

These are the Python tours, that can be browsed as HTML pages, but can also be downloaded as Jupyter notebooks. Please read the installation page for more information about how to run these tours.

Basics

• Introduction to Image Processing
• Le traitement numérique des images
• Image Approximation with Fourier and Wavelets
• Interactive Exploration of Image Approximation
• Wavelet and DCT image compression

Wavelets

• 2-D Daubechies Wavelets
• Volumetric wavelet Data Processing

Approximation, Coding and Compression

• Image Approximation with Orthogonal Bases
• Entropic Coding and Compression

Denoising

• Linear Image Denoising
• Wavelet Denoising
• Wavelet Block Thresholding
• Non Local Means
• Rank Filters for Image Processing
• Stein Unbiased Risk Estimator

Inverse Problems

• Image Deconvolution using Variational Method
• Inpainting using Sparse Regularization
• Performance of Sparse Recovery Using L1 Minimization
• Matrix completion using trace norm regularization

Optimization

• Gradient Descent Methods
• Gradient Descent (by Laurent Condat)
• Forward-Backward Splitting (by Laurent Condat)
• Forward-Backward method on the dual problem (by Laurent Condat)
• Douglas Rachford Proximal Splitting (by Laurent Condat)
• Interior Point Methods
• Smooth Bilevel Programming for Sparse Regularization

Shapes

• Edge Detection
• Active Contours using Parameteric Curves
• Active Contours using Level Sets
• Manifold Learning with Isomap

Audio Processing

• Source Separation with Sparsity

Computer Graphics

• Gaussian Models for Texture Synthesis
• Fluid Dynamics

Mesh Parameterization and Deformation

• Mesh Parameterization
• Mesh Denoising

Geodesic Processing

• Dijkstra and Fast Marching Algorithms

Optimal Transport

• Optimal Transport with Linear Programming
• Entropic Regularization of Optimal Transport
• Advanced Topics on Sinkhorn Algorithm
• Semi-discrete Optimal Transport
• Unbalanced Optimal Transport

Machine Learning

• PCA, Nearest-Neighbors and Clustering
• Linear Regression, Ridge and Lasso
• Logistic Classification
• Stochastic Gradient descent
• Simplex Regression with Trend Filtering
• Multi-layer Perceptron
• Deep Neural Networks
• Texture Synthesis using Deep Neural Networks
• Particle Systems Evolutions
• Conformal Prediction
• Denoising Diffusion Models