Unsupervised Microscopy Video Denoising

Mary Aiyetigbo, Alexander Korte, Ethan Anderson, Reda Chalhoub, Peter Kalivas, Feng Luo, Nianyi Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6874-6883

In this paper we introduce a novel unsupervised network to denoise microscopy videos featured by image sequences captured by a fixed location microscopy camera. Specifically we propose a DeepTemporal Interpolation method leveraging a temporal signal filter integrated into the bottom CNN layers to restore microscopy videos corrupted by unknown noise types. Our unsupervised denoising architecture is distinguished by its ability to adapt to multiple noise conditions without the need for pre-existing noise distribution knowledge addressing a significant challenge in real-world medical applications. Furthermore we evaluate our denoising framework using both real microscopy recordings and simulated data validating our outperforming video denoising performance across a broad spectrum of noise scenarios. Extensive experiments demonstrate that our unsupervised model consistently outperforms state-of-the-art supervised and unsupervised video denoising techniques proving especially effective for microscopy videos.