Noise (video)
Related papers: 20
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Video noise refers to random variations in brightness, color, or intensity values across frames captured by cameras and visual sensors, arising from sources such as sensor limitations, low light conditions, compression artifacts, or electromagnetic interference. In robotics and AI, video noise is a pervasive challenge that degrades the quality of raw image data before it can be used for tasks like object detection, pose estimation, odometry, and simultaneous localization and mapping (SLAM). Algorithms that process visual input — from RGB-D cameras like the Kinect to standard optical sensors — must account for or filter out this stochastic corruption to produce reliable results. Techniques such as Kalman filtering, Gaussian process regression, and deep learning-based preprocessing are commonly applied to mitigate its effects. Understanding and modeling video noise matters because unaddressed corruption propagates errors through downstream perception and control pipelines, undermining robot safety and task performance. It is also central to the "reality gap" in simulation-to-real transfer, where synthetic training data must be augmented with realistic noise models to ensure learned behaviors generalize to physical deployments.
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