On this page, you should find all the information about exercises of the Digital Image Processing course.
Evaluation - how can you earn a credit
During the exercises, we will cover some topic of digital image processing. Your task is complete them and handover them for final grading.
|Discrete Fourier Transform||4|
|Inverse Discrete Fourier Transform||4|
|Filtering in frequency domain||3|
|Lens distortion removal||3|
A simple tutorial describing how to program with the OpenCV is provided in the following link: Introduction to OpenCV
You can use a project for the Visual Studio 2017 in the Windows environment to code the exercise.
You can also use a project from CodeBlocks in the Linux environment.
Color image of Lena that we can use for our experiments.
You can also read explanatory text that describes the first exercise in detail. Basic operations with images are described.
Grayscale image Moon that we can use for our experiments with Gamma correction and contrast enhancement.
We'll implement a convolution algorithm. You can apply Box blur, Gaussian blur, Laplace, or other matrices as a convolution mask.
Filtering using anisotropic diffusion (see instructions).
In this exercise, we will compute the Inverse Discrete Fourier Transform. Description of the algorithm is provided in this PDF.
A simple template program is provided.
Image for histogram equalisation: uneq.jpg
How it should look like: screenshot
Instructions how to implement.
Use this text that describes the projective transform.
Color image of a valve (already available in the provided project) that we can use for our experiments.
We'll implement x and y derivatives of image and visualize edge magnitudes of the valve image:
We'll implement a Laplace operator of the valve image:
Edge simplification & double thresholding on the valve image: