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Si-Yao et al., 2019 - Google Patents

Understanding kernel size in blind deconvolution

Si-Yao et al., 2019

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Document ID
1159059733717696333
Author
Si-Yao L
Ren D
Yin Q
Publication year
Publication venue
2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

External Links

Snippet

Most blind deconvolution methods usually pre-define a large kernel size to guarantee the support domain. Blur kernel estimation error is likely to be introduced, yielding severe artifacts in deblurring results. In this paper, we first theoretically and experimentally analyze …
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Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
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    • G06T2207/20182—Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
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