Global patch search boosts video denoising

With the increasing popularity of mobile imaging devices and the emergence of HdR video surveillance, the need for fast and accurate denoising algorithms has also increased. Patch-based methods, which are currently state-of-the-art in image and video denoising, search for similar patches in the signal. This search is generally performed locally around each target patch for obvious complexity reasons. We propose here a new and efficient approximate patch search algorithm. It permits for the first time to evaluate the impact of a global search on the video denoising performance. A global search is particularly justified in video denoising, where a strong temporal redundancy is often available. We first verify that the patches found by our new approximate search are far more concentrated than those obtained by exact local search, and are obtained in comparable time. To demonstrate the potential of the global search in video denoising, we take two patch-based image denoising algorithms and apply them to video. While with a classical local search their performance is poor, with the proposed global search they even improve the latest state-of-the-art video denoising methods.