Jiang He, Wu Peilin, Zheng Zhou, Gu Hao, Yi Fudi, Cui Wen, Lv Chen
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
School of Software, Taiyuan University of Technology, Taiyuan 030024, China.
Sensors (Basel). 2025 Sep 6;25(17):5567. doi: 10.3390/s25175567.
Video denoising in extremely low-light surveillance scenarios is a challenging task in computer vision, as it suffers from harsh noise and insufficient signal to reconstruct fine details. The denoising algorithm for these scenarios encounters challenges such as the lack of , and the noise distribution in the real world is far more complex than in a normal scene. Consequently, recent state-of-the-art (SOTA) methods like VRT and Turtle for video denoising perform poorly in this low-light environment. Additionally, some methods rely on raw video data, which is difficult to obtain from surveillance systems. In this paper, a denoising method is proposed based on the trilateral filter, which aims to denoise real-world low-light surveillance videos. Our trilateral filter is a weighted filter, allocating reasonable weights to different inputs to produce an appropriate output. Our idea is inspired by an experimental finding: noise on stationary objects can be easily suppressed by averaging adjacent frames. This led us to believe that if we can track moving objects accurately and filter along their trajectories, the noise may be effectively removed. Our proposed method involves four main steps. First, coarse motion vectors are obtained by bilateral search. Second, an amplitude-phase filter is used to judge and correct erroneous vectors. Third, these vectors are refined by a full search in a small area for greater accuracy. Finally, the trilateral filter is applied along the trajectory to denoise the noisy frame. Extensive experiments have demonstrated that our method achieves superior performance in terms of visual effects and quantitative tests.
在极低光照监控场景下的视频去噪是计算机视觉中的一项具有挑战性的任务，因为它会受到严重噪声和信号不足的影响，难以重建精细细节。针对这些场景的去噪算法面临诸多挑战，例如缺乏……，而且现实世界中的噪声分布远比正常场景复杂得多。因此，诸如VRT和Turtle等近期的视频去噪先进（SOTA）方法在这种低光照环境下表现不佳。此外，一些方法依赖原始视频数据，而从监控系统中很难获取这些数据。本文提出了一种基于三边滤波器的去噪方法，旨在对现实世界中的低光照监控视频进行去噪。我们的三边滤波器是一种加权滤波器，为不同输入分配合理权重以产生合适的输出。我们的想法受到一项实验结果的启发：通过对相邻帧进行平均可以轻松抑制静止物体上的噪声。这使我们相信，如果能够准确跟踪运动物体并沿其轨迹进行滤波，可能会有效去除噪声。我们提出的方法包括四个主要步骤。首先，通过双边搜索获得粗略的运动向量。其次，使用幅度 - 相位滤波器来判断和纠正错误向量。第三，在小区域内进行全搜索以进一步细化这些向量，从而提高准确性。最后，沿着轨迹应用三边滤波器对有噪声的帧进行去噪。大量实验表明，我们的方法在视觉效果和定量测试方面都取得了优异的性能。
