Unsupervised Microscopy Video Denoising
<|reference_start|>Unsupervised Microscopy Video Denoising: 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.<|reference_end|>
@article{aiyetigbo2024unsupervised,
    title={Unsupervised Microscopy Video Denoising},
    author={Mary Aiyetigbo, Alexander Korte, Ethan Anderson, Reda Chalhoub, Peter
  Kalivas, Feng Luo, Nianyi Li},
    journal={arXiv preprint arXiv:2404.12163},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12163},
    primaryClass={eess.IV cs.CV}
}