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- W1972249737 abstract "In this paper, a highly effective and efficient ensemble learning based parallel impulse noise detection algorithm isproposed. The contribution of this paper is three-fold. First, we propose a novel intensity homogeneity metric-Directional Homogeneity Descriptor(DHD), which has very powerful discriminative ability and has been provenin our previous work. Second, this proposed algorithm has high parallelism in feature extraction stage, classifiertraining, and testing stage. And the proposed architecture are very suitable for distributed processing. Finally,instead of manually tune the thresholds for each feature as most of the works in this research area do, weuse Random Forest to make decision since it has been demonstrated to own better generalization ability andperformance comparable to SVM or Boosting in classification problem. Another important reason we adoptRandom Forest is that it has natural parallelism structure and very significant performance advantage (e.g. theoverhead of training and testing the model is very low ) over other popular classifiers e.g. SVM or Boosting.To the best of our knowledge, this is the first time ensemble learning strategies have been used in the areaof switching median filtering. Extensive simulations are carried out on several most common standard testingimages. The experimental results show that our algorithm achieves zero miss detection results while keeping thefalse alarm rate at a rather low level and has great superiority over other state-of-the-art methods." @default.
- W1972249737 created "2016-06-24" @default.
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- W1972249737 date "2011-01-23" @default.
- W1972249737 modified "2023-09-23" @default.
- W1972249737 title "A parallel impulse-noise detection algorithm based on ensemble learning for switching median filters" @default.
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- W1972249737 doi "https://doi.org/10.1117/12.872262" @default.
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