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Differential Video Noise Estimation

Authors: Zhu Lei; Xu Peixia;

Differential Video Noise Estimation

Abstract

In this paper, we propose a fast and reliable white- noise variance estimation. The method subtracts two sequential frames of video first and then finds intensity-homogeneous blocks in both original image and differential image, and at last estimates the noise variance in these blocks by a Gaussian weighted averaging process. Experiments show that the proposed method performs well both in highly noisy and good- quality images. It also works well in videos including rich motion, large textured areas and few uniform blocks.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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