[français]
Charles Deledalle - Teaching
- UCSD ECE285 Machine learning for image processing
- UCSD ECE285 Image and video restoration
- UCSD KPU's Smart IoT Workshop (Summer 2019)
- Miscellaneaous
UCSD ECE285 Machine learning for image processing
Spring 2019 version: click here.
Lectures
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Assignments (individual)
- Assignment 0 - Python, Numpy and Matplotlib (prereq, optional)
- Assignment 1 - Backpropagation
- Assignment 2 - CNNs and Pytorch
- Assignment 3 - Transfer Learning
- Assignment 4 - Image Denoising
- MNIST: dataset, MNISTtools.py
- Caltech-UCSD-Birds: dataset, train.csv, val.csv
- BSDS: dataset
- nntools package: nntools.py
Projects (pick only one of them, by groups of 3/4)
- Project A - Image Captioning
- Project B - Style Transfer
- Project C - Multi-Object detection
- Project D - Open-ended
Supplemtary meterials
Everything else
- Fall 2019: enroll to Piazza ECE285 MLIP FA19. Access Google Calendar.
- Spring 2019: enroll to Piazza ECE285 MLIP SP19. Access Google Calendar.
- Fall 2018: enroll to Piazza ECE285 MLIP FA18
- Spring 2018: Go to TritonEd
UCSD ECE285 Image and video restoration
Fall 2017 Matlab version: click here.
Lectures
![]() Image sciences, image processing, image restoration, photo manipulation. Image and videos representation. Digital versus analog imagery. Quantization and sampling. Sources and models of noises in digital CCD imagery: photon, thermal and readout noises. Sources and models of blurs. Convolutions and point spread functions. Overview of other standard models, problems and tasks: salt-and-pepper and impulse noises, half toning, inpainting, super-resolution, compressed sensing, high dynamic range imagery, demosaicing. Short introduction to other types of imagery: SAR, Sonar, ultrasound, CT and MRI. Linear and ill-posed restoration problems. |
![]() Moving averages. Finite differences and edge detectors. Gradient, Sobel and Laplacian. Linear translations invariant filters, cross-correlation and convolution. Adaptive and non-linear filters. Median filters. Morphological filters. Local versus global filters. Sigma filter. Bilateral filter. Patches and non-local means. Applications to image denoising. |
![]() Fourier decomposition and Fourier transform. Continuous verse discrete Fourier transform. 2D Fourier transform and spectral analysis. Low-pass and high-pass filters. Convolution theorem. Image sharpening, Image resizing and sub-sampling. Aliasing, Nyquist -Shannon theorem, zero-padding, and windowing. Spectral models of sub-sampling in CT and MRI. Radon transform, k-space trajectories, and streaking artifacts. |
![]() Heat equation. Discretization and finite difference. Explicit and implicit Euler schemes. CFL conditions. Continuous Gaussian convolution solution. Linear and non-linear scale spaces. Anisotropic diffusion. Perona-Malik and Weickert model. Variational methods. Tikhonov regularization by gradient descent. Links between variational models and diffusion models. Total-Variation regularization and ROF model. Sparsity and group sparsity. Applications to image deconvolution. |
![]() Sample mean, law of large numbers, and method of moments. Mean square error and bias-variance trade-off. Unbiased estimation: MVUE, Cramèr-Rao-Bound, Efficiency, MLE. Linear estimation: BLUE, Gauss-Markov theorem, least-square error estimator, Moore-Penrose pseudo-inverse. Bayesian estimators: likelihood and priors, MMSE and posterior mean, MAP. Linear MMSE, applications to Wiener deconvolution, image filtering with PCA, and the non-local Bayes algorithm. Applications to image denoising. |
![]() Limits of Wiener filtering. Non-linear shrinkage functions. Limits of Fourier representation. Continuous and discrete wavelet transforms. Sparsity and shrinkage in wavelet domain. Undecimated wavelet transforms, a trous algorithm. Regularization. Sparse regression, combinatorial optimization and matching pursuit. LASSO, non-smooth optimization, and proximal minimization. Link with implicit Euler scheme. ISTA algorithm. Synthesis versus analysis regularized models. Applications to image deconvolution. |
![]() Patch models and sparse decompositions of image patches. Dictionary learning and the k-SVD algorithm. Collaborative filtering and BM3D. Non-local sparse based models. Expected patch log-likelihood. Other applications of patch models in inpainting, super-resolution and deblurring. |
Assignments in Python (individual)
- Assignment 0 - Python, Numpy and Matplotlib (prereq, optional)
- Assignment 1 - Watermarking
- Assignment 2 - Basic Image Tools
- Assignment 3 - Basic Filters
- Assignment 4 - Non-local means
- Assignment 5 - Fourier transform
- Assignment 6 - Wiener deconvolution
- Archive: ece285_IVR_assignments.zip
Projects in Python (pick only one of them, by groups of 2/3)
- Project A - Anisotropic Diffusion
- Project B - Total-Variation
- Project C - Wavelets
- Project D - Non-local regularization
Everything else
- Spring 2019: enroll to Piazza ECE285 MLIP SP19. Access Google Calendar.
Miscellaneaous
- Submitting Notebook to Gradescope as PDF
- Setting up Python and Jupyter with Conda environments on Linux or Max OS-X
- Setting up Python, PyTorch and Jupyter on Windows
- Transferring files from and to an UCSD's DSMLP Pod session
- On the convergence of gradient descent
- Cookbook for data scientists
- Documentation for Git: https://www.atlassian.com/git/tutorials
Last modified: Wed Jan 29 08:57:06 UTC 2020