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Related Experiment Video

Updated: Jan 18, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

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Published on: March 6, 2014

Tracking-Based Denoising: A Trilateral Filter-Based Denoiser for Real-World Surveillance Video in Extreme Low-Light

He Jiang¹, Peilin Wu¹, Zhou Zheng¹

¹School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.

Sensors (Basel, Switzerland)

|September 13, 2025

Summary

This study introduces a novel trilateral filter for low-light video denoising in surveillance. The method effectively removes noise by tracking object trajectories, outperforming current state-of-the-art techniques.

Keywords:

amplitude-phase filter low light surveillance video trilateral filter video denoising

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Area of Science:

Computer Vision
Image Processing

Background:

Low-light video denoising is challenging due to noise and lack of ground truth.
Existing methods like VRT and Turtle struggle with real-world surveillance data complexity.
Many approaches require raw video data, unavailable in surveillance systems.

Purpose of the Study:

To develop an effective video denoising method for extremely low-light surveillance scenarios.
To address limitations of current state-of-the-art methods in complex noise environments.
To propose a solution that does not rely on raw video data.

Main Methods:

A novel trilateral filter-based denoising approach is proposed.
The method utilizes object trajectory filtering inspired by noise suppression on stationary objects.
Key steps include motion vector estimation, correction, refinement, and trilateral filtering along trajectories.

Main Results:

The proposed trilateral filter method demonstrates superior performance in visual quality.
Quantitative tests confirm the effectiveness of the denoising technique.
The method achieves better results compared to VRT and Turtle in low-light surveillance.

Conclusions:

The trilateral filter method offers a robust solution for low-light surveillance video denoising.
Accurate motion tracking and trajectory-based filtering are crucial for effective noise removal.
The proposed approach overcomes limitations of existing methods for real-world surveillance applications.