Each lecture is delivered in the form of a seminar and precedes a practical hackathon on the same topic, providing the theoretical framework. Students are introduced to the motivation, an overview of existing methods, and the principles of fundamental approaches. Selected advanced methods are explored in depth through student presentations, followed by instructor commentary and the supplementation of key information necessary for understanding the material.
1. Advanced image denoising – overview of image noise, principles of variational and diffusion-based methods, the role of regularisation, and deep learning possibilities.
2. Image restoration and inverse problems – degradation models, inverse problems, regularisation principles, and Bayesian approaches.
3. Keypoint detection and local features – detection and description of keypoints, invariance, and robust matching.
4. Stereo and multiview processing – epipolar geometry, camera calibration principles, basics of disparity maps, and 3D reconstruction.
5. Advanced image registration – rigid and non-rigid registration methods, similarity metrics, and optimisation strategies.
6. Motion analysis and object tracking – principles of optical flow, object tracking, motion models, and handling of partial or complete occlusion.
7. Advanced image segmentation – energy-based and graph-based models, Markov random fields, and principles of multi-label and hybrid segmentation.

The practical exercises are organised in the form of a hackathon, with each topic representing a distinct challenge. All students work on the same assignment and aim to solve the given problem as efficiently and creatively as possible. The exercises are carried out in groups, fostering teamwork, discussion, and idea sharing, while providing the opportunity to apply the knowledge gained during the seminars in a practical setting.
1. Advanced image denoising – application of methods for noise removal, comparison of different approaches, and evaluation of quality and robustness of results.
2. Image restoration and inverse problems – application of image restoration techniques, experimentation with different approaches, and assessment of restoration quality.
3. Keypoint detection and local features – working with keypoints and local features, and their use in solving other image processing tasks.
4. Stereo and multiview processing – obtaining 3D information from multiple views and evaluating reconstruction accuracy.
5. Advanced image registration – application of registration methods, optimisation of the process, and assessment of registration accuracy.
6. Motion analysis and object tracking – analysis of motion in image sequences, implementation of tracking algorithms, including error handling.
7. Advanced image segmentation – application of image segmentation methods and evaluation of reliability and accuracy of the results.

Students are expected to dedicate sufficient time to preparing a clear and detailed presentation on the given topic. Preparation includes independently studying the subject matter, reviewing relevant literature and resources, and consulting with the instructor as needed to fully understand the material and create a high-quality presentation. The presentation should be prepared in such a way and with such depth that it allows other students to easily orient themselves in the topic. The goal is for students to structure the presentation clearly, understand the details of the methods, and be able to discuss possible approaches and applications.