Unsupervised Coordinate-Based Video Denoising

TL;DR

This paper presents an unsupervised deep learning approach for video denoising that leverages coordinate-based networks to effectively remove noise from real-world videos without prior noise knowledge, simplifying the model while preserving details.

Contribution

The proposed method introduces a novel coordinate-based network architecture with three modules for unsupervised video denoising, improving robustness and detail preservation without requiring noise model knowledge.

Findings

Effective denoising of real-world calcium imaging videos

No need for noise model prior or data augmentation

Simplified network structure with high-frequency detail preservation

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.