Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ViDeNN: Deep Blind Video Denoising   We propose ViDeNN: a CNN for Video Denoising without prior knowledge on the
noise distribution (blind denoising). The CNN architecture uses a combination
of spatial and temporal filtering, learning to spatially denoise the frames
first and at the same time how to combine their temporal information, handling
objects motion, brightness changes, low-light conditions and temporal
inconsistencies. We demonstrate the importance of the data used for CNNs
training, creating for this purpose a specific dataset for low-light
conditions. We test ViDeNN on common benchmarks and on self-collected data,
achieving good results comparable with the state-of-the-art.

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The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: eess