Attacking Defocus Detection With Blur-Aware Transformation for Defocus Deblurring

Previous fully-supervised defocus deblurring has made significant progress. However, training such deep models requires abundant paired ground truth, which is expensive and error-prone. This paper makes an attempt to train a defocus deblurring model without using paired ground truth and any other un...

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Vydané v:	IEEE transactions on multimedia Ročník 26; s. 1 - 11
Hlavní autori:	Zhao, Wenda, Hu, Guang, Wei, Fei, Wang, Haipeng, He, You, Lu, Huchuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Blur-aware transfer Bridges Convolution Defocus detection attack Feature extraction Generative adversarial networks Robustness Task analysis Training Weakly-supervised defocus deblurring
ISSN:	1520-9210, 1941-0077
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