ACIT4710 Digital Signal and Image Processing Course description

Signal processing defines the mathematical tools used to analyze, model and operate on measured data from physical signals and their sources. In digital signal processing (DSP) the following topics are lectured: the different domains for describing discrete signals and linear time invariant systems, digital filters and frequency analysis. Images are two-dimensional signals and the same analytic methods apply. Reconstruction of medical images will be used as practical application.

Signal Processing or knowledge of the Fourier and Laplace transforms. Some knowledge of Matlab progamming.

No formal requirements over and above the admission requirements.

On completion of this course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The course will give the students in depth knowledge on the following topics

• Sampling, digitalization and reconstruction.
• How to obtain the signal spectrum of digital and analog signals
• How to obtain the frequency response of digital and analogue systems
• Filtering
• Detection techniques, correlation and deconvolution
• Reconstruction of images from measured data by inverse methods

Skills

The student will know how to:

• Describe digital signals and systems mathematically in the time domain, the frequency domain and the Z-domain
• Describe digital images mathematically in real space and K-space
• Describe linear time invariant systems using difference equations, impulse responses, point spread functions and transfer functions
• Analyze time discrete systems in the frequency domain and discrete images in K- space
• Use analog filters and the digitized versions of analog filters: FIR and IIR
• Apply post processing of images for filtering
• Apply inverse methods for image reconstruction from measured data

General competence

The student will have general competence on:

• Spectrums, impulse responses, point spread functions, frequency responses, K- space, correlation, convolution and modulation
• Fourier-series (FS), Fourier transform (FT).
• Sampling, reconstruction and aliasing
• Implementation of DSP-filters and inverse methods on a computer.

This course will contain the following topics:

• Sampling, reconstruction and aliasing
• Impulse response, Point spread function
• Fourier series and Fourier transform
• Frequency analysis and k-space analysis
• Frequency response, filters and transfer functions
• Detection methods, correlation, convolution and modulation
• Inverse methods for image reconstruction

Lectures.

Practical training

Computer exercises.

Mandatory computer exercises, individual or in groups (6 hours per week).

Exam autumn 2020 due to Covid-19:

Individual digital home exam, 3 hours.

The exam grade can be appealed.

In the event of a resit or rescheduled exam, an oral examination may be used instead. In such case, the grade cannot be appealed.

[Exam earlier:]

Individual written exam, 3 hours.

The exam grade can be appealed.

In the event of a resit or rescheduled exam, an oral examination may be used instead. In such case, the grade cannot be appealed.

Aids autumn 2020:

All aids allowed, except communication with others

[Aids earlier:]

Calculator that cannot be used to communicate with others.

For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.

One internal examiners. External sensor is used periodically. If oral, two internal examiners.