Six Main Tasks in Image Processing

4 Sept 2025, 10:00 → 16 Oct 2025, 11:30 Europe/Berlin

online

organized in cooperation with Helmholtz Information & Data Science Academy (HIDA) and Helmholtz Imaging

Six Main Tasks in Image Processing 

Applied Imaging Processing - Concepts and Techniques

In this series of seminars, six key image processing tasks will be discussed, following a typical workflow in the image processing pipeline.

Images are not always captured by a camera. Often, they must be tediously reconstructed from a series of projections or other non-image types of acquisitions. Different reconstruction algorithms allow for better image quality or can focus on specific properties of the objects under observation. Noise can be introduced at many steps in the image acquisition process. Denoising is therefore an essential step in most image processing workflows. Tracking individual objects over multiple time steps is a difficult task, but allows for the observation of temporal dynamics. Segmentation refers to the assignment of each pixel in an image to a specific category. In semantic segmentation, all pixels belonging to a cat are labeled "cat", and all pixels belonging to trees are labeled "tree". In instance segmentation, each pixel is additionally assigned to an object instance, making it possible to distinguish multiple cats and trees in an image. The visualization of otherwise difficult-to-interpret data, such as reconstructed 3D(+T) objects or high-dimensional image data, is essential for understanding the results. Finally, interpreting the results of AI-based image analysis algorithms is important: Why was a particular decision made? What structures in the images were responsible? What can AI tell us about the underlying problem?

The course consists of lectures and interactive discussions. It covers various image processing techniques. It is recommended to attend all lectures for a deep understanding of the subject. Registration is required to attend. 

Learning Goals

By the end of the course, you will have a basic understanding of the following six image processing techniques …

• reconstruction

• denoising

• tracking

• segmentation

• visualization

• AI-based image analysis

Prerequisites

To participate in this course, you need to know...

• Master in MINT

Target Group

Anyone interested who is affiliated with Helmholtz or a HIDA partner.

Course Days & Time

Sep 04, 2025, 10 am - 11:30 am

Sep 11, 2025, 10 am - 11:30 am

Sep 18, 2025, 10 am - 11:30 am

Sep 25, 2025, 10 am - 11:30 am

Oct 02, 2025, 10 am - 11:30 am

Oct 09, 2025, 10 am - 11:30 am

Oct 16, 2025, 10 am - 11:30 am

 

NOTE: Registration will open August 7, 2025, 12 pm.

Attendance & Certificates 

The course content is coordinated, so we strongly recommend that you do not miss any part of the course. To receive a certificate we expect at least 80% attendance and active participation. 

Registration & Cancellation

This course is open to individuals affiliated with Helmholtz or a HIDA Partner only.

Your registration for this course is binding. If you need to leave/miss the course for a period of time, please let us know in advance via hida-courses@helmholtz.de.

If you have to cancel the course for any reason, please do so as soon as possible to allow time for others to take your seat. To cancel, please withdraw your registration on the course site or write an email to hida-courses@helmholtz.de. 

Additional Information

There is no waiting list for this course! If someone withdraws from a course, their place is automatically reopened. We therefore advise you to keep an eye on the registration in case the course is fully booked and you would like to attend. Also, this course will be offered again in the future - you can check our HIDA course catalog for updates.  

This course is free of charge. 

Thursday 4 September

10:00 → 11:30 The six main tasks in image processing: an overview

Modern imaging methods enable us to capture structure and dynamics in unprecedented detail and with high temporal and spatial resolution. The challenge is to make full use of the potential of such "big" imaging data to obtain quantitative results, test hypotheses, and develop new theories and models. Manual or semi-automated image analysis workflows quickly become a bottleneck because they do not scale well with the amount and complexity of the data. This lecture provides an overview of typical automated image analysis workflows using examples from high-resolution microscopy in the life sciences. We will cover the six main tasks, including image reconstruction from raw tomography or localization data, denoising, tracking in time and space, segmentation to extract objects from images, visualization, and explainable AI-based methods. In the last part, we will introduce tools for integrating these different tasks into complete workflows.

Speaker: Phillip Kollmansberger (Heinrich-Heine-Universität Düsseldorf)

Thursday 11 September

10:00 → 11:00 Tomographic methods in medical imaging

In medical imaging, dedicated cameras are used to capture images, and medical images are typically three-dimensional images. These three-dimensional images are generated from a series of datagrams or images of sections and/or projections, which is achieved by tomographic reconstruction. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) are typical examples of different imaging modalities where tomographic reconstruction methods are used. This lecture gives a brief introduction to Fourier transform based reconstruction (MRI), filtered back projection (CT) and iterative reconstruction (PET and CT).

Speaker: Christoph Lerche (FZJ)

Thursday 18 September

10:00 → 11:30 Image Denoising: Supervised, Self-supervised, Generative

Removing imaging noise is an essential problem in scientific applications, where sensors are often pushed to the edge of what is possible. The past years have seen a range of machine learning methods proposed: supervised approaches, using image pairs to learning a mapping from noisy to clean images; Self-supervised approaches, capable of learning such a mapping from noisy data alone; and finally generative approaches capable of additionally capturing the inherent uncertainty of the problem.
In this talk will talk about how these approaches can be understood and derived from a probabilistic perspective.

Speaker: Alexander Krull (University of Birmingham)

Thursday 25 September

10:00 → 11:30 Tracking of objects: from one to many

I will talk about two separate research areas for tracking objects over time and instances. The first applies to the scenario of tracking an object over time, e.g. a known 3D rigid model. I will introduce Bayesian and particle filters and explain some technical ideas to make them fast and accurate. In the second part of the lecture I will introduce the field of tracking a large set of objects (e.g. cells) over time, and also instances. Since the objects typically do not move randomly, it makes sense to formalize their "structured motion" and formulate this task as a structured prediction problem. To this end, I will introduce efficient solvers for this problem.

Speaker: Carsten Rother (Heidelberg University)

Thursday 2 October

10:00 → 11:30 Explainable Machine Learning

Explainable machine learning involves two complementary types of questions: (1) Method designers and theorists ask: "Why do deep neural networks work so well? How can we further improve them? How can we provide formal performance guarantees?" (2) Instead, method users ask, "How did the network arrive at its conclusion? What variables determine the result, and in what way? Can the result be trusted?" The talk will introduce both areas and review the current state of our answers.

Speaker: Ullrich Koethe (Heidelberg University)

Thursday 9 October

10:00 → 11:30 Visualizing spatial datasets

Join us in our journey through selected methods of telling the story of your dataset through visualizations. In the seminar, we will transform 3D segmentations into effectful Blender renderings.

Speaker: Deborah Schmidt (MDC)

Thursday 16 October

10:00 → 11:30 Machine Learning for Image Segmentation

The course will introduce current machine learning models for image segmentation, including semantic- and instance segmentation. It will also cover how to apply such models to large data. A respective tutorial at the end of the seminar series will give hands-on experience in training and applying such models.

Speaker: Dr Dagmar Kainmüller (MDC Berlin)