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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.2978645
|2 doi
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Yang, Xuhui
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Image Denoising via Sequential Ensemble Learning
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|c 2020
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|a Text
|b txt
|2 rdacontent
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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 Image denoising is about removing measurement noise from input image for better signal-to-noise ratio. In recent years, there has been great progress on the development of data-driven approaches for image denoising, which introduce various techniques and paradigms from machine learning in the design of image denoisers. This paper aims at investigating the application of ensemble learning in image denoising, which combines a set of simple base denoisers to form a more effective image denoiser. Based on different types of image priors, two types of base denoisers in the form of transform-shrinkage are proposed for constructing the ensemble. Then, with an effective re-sampling scheme, several ensemble-learning-based image denoisers are constructed using different sequential combinations of multiple proposed base denoisers. The experiments showed that sequential ensemble learning can effectively boost the performance of image denoising
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|a Journal Article
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| 700 |
1 |
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|a Xu, Yong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Quan, Yuhui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ji, Hui
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2020) vom: 11. März
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
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|g year:2020
|g day:11
|g month:03
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|u http://dx.doi.org/10.1109/TIP.2020.2978645
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