Combining denoising and object detection
An analysis to provide insights in combining denoising with object detection
R.M. Huizer (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Osman Semih Kayhan – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
J.C. Van Gemert – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
N. Yorke-Smith – Graduation committee member (TU Delft - Algorithmics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Automated imaging systems, critical in domains like medical imaging, autonomous driving, and security, experience noise from camera sensors and electronic circuits in bad or dark lighting conditions. This impacts downstream tasks, including object detection. However, an analysis of strategies combining denoising and object detection is lacking. This study addresses this gap by analyzing diverse strategies for optimizing both image quality and detection performance. Results reveal that isolating denoiser network optimization and training a detector on its outputs yields the best overall performance. Combining detection and denoising enhances detection outcomes. The results offer valuable insights to make educated decisions on how to combine denoising and detection in modern imaging systems.