Unsupervised Microscopy Video Denoising

arXiv:2404.12163v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cnn cs.cv denoising eess.iv featured filter image interpolation location microscopy network noise novel paper restore signal temporal type types unsupervised video video denoising videos