Authors:
Thibaud Ehret
;
Pablo Arias
and
Jean-Michel Morel
Affiliation:
CMLA and ENS Cachan, France
Keyword(s):
Video Denoising, Patch-based Methods, Patch Search, Nearest Neighbors Search.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
Abstract:
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 a
nd 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.
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