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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.3016134
|2 doi
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|a eng
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| 100 |
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|a Li, Boyun
|e verfasserin
|4 aut
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| 245 |
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|a Zero-Shot Image Dehazing
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|c 2020
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a In this paper, we study two less-touched challenging problems in single image dehazing neural networks, namely, how to remove haze from a given image in an unsupervised and zeroshot manner. To the ends, we propose a novel method based on the idea of layer disentanglement by viewing a hazy image as the entanglement of several "simpler" layers, i.e., a hazy-free image layer, transmission map layer, and atmospheric light layer. The major advantages of the proposed ZID are two-fold. First, it is an unsupervised method that does not use any clean images including hazy-clean pairs as the ground-truth. Second, ZID is a "zero-shot" method, which just uses the observed single hazy image to perform learning and inference. In other words, it does not follow the conventional paradigm of training deep model on a large scale dataset. These two advantages enable our method to avoid the labor-intensive data collection and the domain shift issue of using the synthetic hazy images to address the real-world images. Extensive comparisons show the promising performance of our method compared with 15 approaches in the qualitative and quantitive evaluations. The source code could be found at www.pengxi.me
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|a Journal Article
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|a Gou, Yuanbiao
|e verfasserin
|4 aut
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| 700 |
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|a Liu, Jerry Zitao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhu, Hongyuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Joey Tianyi
|e verfasserin
|4 aut
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| 700 |
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|a Peng, Xi
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2020) vom: 18. Aug.
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|x 1941-0042
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|g volume:PP
|g year:2020
|g day:18
|g month:08
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|u http://dx.doi.org/10.1109/TIP.2020.3016134
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