# Course Info

The purpose of this course is a self-contained introduction to modern techniques in image processing. We will understand concepts involved in acquiring, sampling, representing, compressing, and processing of multi-dimensional signals (images and videos). We will discuss the notion of inverse problems and ways to solve them – from traditional prior-based methods to modern learning-based methods. Finally, apart from the standard image acquisition model, we will also look at more exotic computational and medical imaging schemes and understand the challenges and applications involved there within.

## Learning Outcomes

At the end of the course, the student will:

1. Get comfortable with the standard notions involved in acquiring, sampling, representing, compressing, and processing of images.
2. Understand the evolution of ideas in image processing
3. Know how to implement these state-of-the-art image processing algorithms in Python.
4. Perform a small research project using the studied notions and techniques.

Evaluation: 60% Homework assignments, 40% final project.

Language: The course will be taught in English.

Credits: 3.0.

Lecturer

TA

## Detailed Syllabus

# Date Pre-requisite Lecture Tutorial Homework
1 22/10/2020   Introduction Math background
2 29/10/2020 Lecture 2 Signals & systems, Fourier Convolutions
3 05/11/2020 Lecture 3 (until Poisson summation) Adjoint, Dirac’s delta, Poisson summation Fourier transform
4 12/11/2020 Lecture 3 (the rest) Sampling & interpolation Computerized tomography
5 19/11/2020 Lecture 4 Discrete domain signals & systems Sampling & iterpolation HW1
6 26/11/2020 Lecture 5a Introduction to random signals Inverse filtering
7 03/12/2020 Lecture 5b Inverse problems & statistical estimation ML vs. MAP
8 10/12/2020 Lecture 6 Patch-based priors Bilateral filters, NLM HW2
9 24/12/2020 Lecture 7 Sparsity-based priors PatchMatch
10 31/12/2020 Lecture 8 Structured-based priors Sparse coding, BM3D
11 07/01/2021 Lecture 9 Learning image priors Dictionary learning
12 14/01/2021 Lecture 10 Sampling & sensing Deep learning (part 1)
13 21/01/2021 Lecture 11 Computational imaging Deep learning (part 2)