Unsupervised Microscopy Video Denoising
This addresses a challenge in real-world medical applications by enabling denoising without pre-existing noise distribution knowledge, though it appears incremental as it builds on existing video denoising methods.
The paper tackles the problem of denoising microscopy videos corrupted by unknown noise types, introducing an unsupervised network that outperforms state-of-the-art supervised and unsupervised techniques across various noise scenarios.
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.