This is the Julia collection. For complete Python walkthroughs, visit the Python tours. All language archives →
These are the Julia 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
Wavelets
Approximation, Coding and Compression
Denoising
- Linear Image Denoising
- Wavelet Denoising
- Wavelet Block Thresholding
- Non Local Means
- Rank Filters for Image Processing
Inverse Problems
- Image Deconvolution using Variational Method
- Inpainting using Sparse Regularization
- Performance of Sparse Recovery Using L1 Minimization
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)
- Chambolle-Pock Primal-Dual Splitting Algorithm
Machine Learning
- PCA, Nearest-Neighbors and Clustering
- Linear Regression and Kernel Methods
- Logistic Classification
- Stochastic Gradient descent
Shapes
- Edge Detection
- Active Contours using Parameteric Curves
- Active Contours using Level Sets
- Manifold Learning with Isomap