Unsupervised Microscopy Video Denoising
Mary Aiyetigbo Alexander Korte Ethan Anderson Reda Chalhoub Peter Kalivas Feng Luo Nianyi Li
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.