Single-Course English 5 ECTS

Computational imaging and spectroscopy

Overall Course Objectives

The principal objective of this course is to expose the necessary mathematical and computational methods to bridge the gap between optics and image processing, and perform image analysis and acquisition in the context of computer vision and computational imaging. The course will cover applications e.g. in optics, hyperspectral imaging, imaging spectroscopy, medical imaging, computational imaging, or computer vision and pattern recognition. The course is split between theory, exercises and projects, allowing the students to get hands on experience on the taught methods.

Learning Objectives

  • Apply the concepts of Harmonic analysis and Compressed Sensing to imaging
  • Design computer vision and computational imaging systems and algorithms
  • Analyze and process signals from cameras and optical sensors
  • Apply the concepts of inverse problems and linear optimization to imaging
  • Recover, analyze and process spectrally resolved data from optical sensors or images
  • Recover and analyze scene physics from optical sensors or digital images
  • Apply Deep Learning and Machine Learning frameworks to solve computational imaging problems
  • Process and analyze hyperspectral images with Machine Learning methods

Course Content

The course presents a wide range of computational methods, classic and novel, used for analysis of digital images. It covers for instance recovering data or physical information from sparse or dense measurements, reconstructing and restoring images, performing automated image analysis such as facial recognition or AI aided medical diagnosis, and designing advanced imaging systems with applications in e.g. spectroscopy.

The course consists of five parts:
1. Introduction to digital imaging
2. Sparse representations and image restoration
3. Scene analysis and spectral imaging
4. Introduction to deep learning for computational imaging
5. Computational spectroscopy and hyperspectral image analysis

The course focuses first on traditional digital imaging: after presenting the fundamentals of colorimetry, the functional principles of sensing devices used for image acquisition and classic image processing tools, such as kernel filtering, are covered. Then advanced harmonic analysis methods will be introduced to exploit the sparse nature of digital images, with applications in image restoration, time and frequency analysis, and compression. This will be followed by an overview of state of the art image restoration methods, including sparse optimization methods, numerical methods for image inpainting and classic filter based approaches for denoising. Spectral imaging and scene physics analysis will then be presented with the motivation to retrieve scene physics and spectral information from digital imaging. Afterwards, deep learning methods will be introduced as an overview of concepts and architectures for application to image analysis. Finally, we will address the applications of deep learning and sparse optimization to imaging spectroscopy and hyperspectral imaging analysis, with a focus in medical imaging.

Recommended prerequisites

Matlab or Python programming

Teaching Method

Lectures, exercises/problem solving, project

See course in the course database.





3 weeks




DTU Lyngby Campus

Course code 34269
Course type Candidate
Semester start Week 28
Semester end Week 30
Days Mon-fri 8:00-17:00

7.500,00 DKK