Year: 2024
Type: conference-paper
Abstract: In this paper, we introduce a novel unsupervised video denoising deep learning approach that can help to mitigate data scarcity issues and show robustness against different noise patterns, enhancing its broad applicability. Our method comprises three modules: a Feature generator creating feature maps, a Denoise-Net generating denoised but slightly blurry reference frames, and a Refine-Net re-introducing high-frequency details. By leveraging the coordinate-based network, we can greatly simplify the network structure while preserving high-frequency details in the denoised video frames. Extensive experiments on both simulated and real-captured videos demonstrate that our method can effectively denoise real-world calcium imaging video sequences without prior knowledge of noise models and data augmentation during training.
Institutions: Clemson University, Medical University of South Carolina
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4402915284