Classic Video Denoising in a Machine Learning World: Robust, Fast, and Controllable
This addresses the need for reliable, fast, and controllable denoising in video processing, offering an incremental improvement over existing methods.
The paper tackles the problem of video denoising by bridging traditional and deep learning methods, proposing a differentiable pipeline where a neural network predicts optimal parameters, resulting in a robust and efficient approach that supports user control.
Denoising is a crucial step in many video processing pipelines such as in interactive editing, where high quality, speed, and user control are essential. While recent approaches achieve significant improvements in denoising quality by leveraging deep learning, they are prone to unexpected failures due to discrepancies between training data distributions and the wide variety of noise patterns found in real-world videos. These methods also tend to be slow and lack user control. In contrast, traditional denoising methods perform reliably on in-the-wild videos and run relatively quickly on modern hardware. However, they require manually tuning parameters for each input video, which is not only tedious but also requires skill. We bridge the gap between these two paradigms by proposing a differentiable denoising pipeline based on traditional methods. A neural network is then trained to predict the optimal denoising parameters for each specific input, resulting in a robust and efficient approach that also supports user control.