Bachelor-/ Master Thesis/ Internship: A new loss function for Spatio-Temporal Denoising based on analyzing noise characteristics

28.05.2025, Diplomarbeiten, Bachelor- und Masterarbeiten

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

From the published literature, it has been proven that deep learning-based image denoising algorithms outperform the traditional denoising algorithms. However, there are issues that are still unsolved when denoising images for high image quality in terms of human perception. Both traditional approaches and ML based models often do not produce results that look natural and overall pleasing. Especially in the movie making industry, quality expectations are extremely high and most of the denoising results from literature would not be accepted.

Video denoising application is additionally challenging from the image denoising because Human Visual System will find out any temporal inconsistencies if the frames (in a video sequence) are not correctly correlated. Therefore, temporal consistency is paramount for a video denoising algorithm.

Lately, at ARRI we did some work on deep learning-based video denoising which was to understand the best performance we can expect from a deep learning model. A new loss function was tried out and the infrastructure for the training set up. This thesis should cover an in-depth study of this new loss, optimizing it for image quality indistinguishable from real high quality camera images. Additionally, this thesis will include model architecture optimization to achieve the target noise distribution defined by the loss.

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

Contact: Dr. Tamara Seybold

Kontakt: tseybold@arri.de