TSBB14 · 6 credits · Basic level (G2X) · Bioteknik, Elektroteknik

Signal and Image Processing

Active · next start VT 2027

48%

passed the latest ordinary exam

TENA · 27 Mar 2026 · 25 students

64%

passed the ordinary exams, all sittings over the last 5 years

TENA · 161 students

Unusually hard in 2025/26

48% passed the ordinary exam in 2025/26 (25 students), against 65% on average in earlier years (136 students).

Part of 1 programme

Exam

64% passed · 161 students

64% passed · 58 students

Pass the re-exam: 64% (58 students), compared with 64% (161 students) on the ordinary exam
Get 4 or 5: 32% (219 students)

Show small sittings (4)

Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

Partial exams, hand-ins and labs. They are not part of the figures at the top.

Hand-in or lab

100% passed · 156 students

No sittings with at least 5 students.

Show small sittings (1)

Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

After completing the course, the goals below must be met.

The student should be able to explain parts of the basic theory for continuous one-dimensional signals, in both the time and frequency domain (Fourier domain). The student must be able to perform related calculations.
The student should be able to explain parts of the basic theory for discrete signals and systems, for example, sampling, discrete Fourier transform, and z-transform. The student must be able to perform related calculations.
The student should be able to explain parts of the basic theory for digital images (two-dimensional signals), for example, 2D Fourier transforms, 2D discrete convolution, resampling, and interpolation, as well as some classic image analysis. The student must be able to perform related calculations.
The student should be able to analyze signals and images on a computer, implement this in a high-level language such as Matlab, and solve an applied problem in technology, medicine, or biology related to the course content.

1D continuous signals: General about signals and their properties. Briefly about noise. Fourier series. Convolution. The Fourier transform with associated theorems. The Dirac impulse. 1D correlation. Linear continuous-time systems. System properties such as linearity and time invariance.
Sampling and 1D discrete signals: Sampling and reconstruction (also in 2D). Briefly about TDFT. DFT. Linear discrete-time systems and the z-transform. System properties such as causality and stability.
2D signal processing and image analysis: 2D Fourier transform. 2D DFT. Linear operations on digital images, such as convolution, translation, scaling, derivative, and rotation. Convolution kernels in the spatial domain and the Fourier domain: low-pass (Gaussian), high-pass (Laplace), derivative (Sobel). Edge detection using the magnitude of the gradient. Resampling and interpolation. Histogram and thresholding. Binary images and metrics in images. 2D correlation.
Application examples: Signal processing of ECG-signal, computed tomography (CT), ultrasound images, 3-D visualization, JPEG image compression, phase vocoder, resampling of images, and image analysis applications like ‘counting of blood cells’, ‘segmentation of cell kernels’ and ‘measurements on plants’.