Bachelor-/ Master Thesis/ Internship: ML-based image processing versus classical image processing: comparing image quality over computational effort

28.05.2025, Abschlussarbeiten, Bachelor- und Masterarbeiten

External thesis or internship at Arnold & Richter Cine Technik GmbH & Co. Betriebs KG

Lately, learning based image processing algorithms have successfully proven their superiority in terms of resulting image quality compared to the classical image processing algorithms. However, mostly the computational ressources of the inference clearly exceed the ressources needed to apply a classical algorithm. In real applications, resources are limited.

I this thesis, we investigate learning based models with small number of parameters, and compare the results to the results of available classical algorithms. Additionally we study a range of model sizes and use a classic algorithm with scalable computational effort and study how the image quality vs. computational effort quotion compares between classic approach and ML based approach.

In more detail, this project investigates different scalable setups that are feasible on a real camera system. Mainly two different directions should be compared:

- Learning/optimizing the parameters of two different available classical architectures to an appropriate image quality metric

- Learning a Network directly

The results will contribute to answering the question, for which computational power neural network based models can be suited: We expect that for extremely small ressources, classical approaches show higher quality than a very small network. For bigger models we expect that the quality is not only better overall (this is known by SOTA), but also the quality vs computational load is better.

Details of the thesis scope can be adjusted to the kind of thesis (BT, MT, internship).

Contact: Dr. Tamara Seybold

Kontakt: tseybold@arri.de