Unsupervised Coordinate-Based Video Denoising

• URL: http://arxiv.org/abs/2307.00179v1
• Date: Sat, 1 Jul 2023 00:11:40 GMT
• Title: Unsupervised Coordinate-Based Video Denoising
• Authors: Mary Damilola Aiyetigbo, Dineshchandar Ravichandran, Reda Chalhoub, Peter Kalivas, Nianyi Li
• Abstract summary: We introduce a novel unsupervised video denoising deep learning approach that can help to mitigate data scarcity issues. 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.
• Score: 2.867801048665443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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