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Supplementary Video - Unsupervised Coordinate-Based Video Denoising

Citation Author(s):
Dineshchandar Ravichandran, Reda Chalhoub, Peter Kalivas, Feng Luo, Nianyi Li
Submitted by:
Mary Aiyetigbo
Last updated:
8 February 2024 - 12:14am
Document Type:
Supplementary material

In this paper, we introduce a novel unsupervised video denoising deep learning approach that can help to mitigate data scarcity issues and shows robustness against different noise patterns, enhancing its broad applicability. Our method comprises three modules: a Feature generator creating features 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 demonstrate that our method can effectively denoise real-world calcium imaging video sequences without prior knowledge of noise models and data augmentation during training.

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