基于K近邻算法的噪声种类识别和强度估计

doi:10.11772/j.issn.1001-9081.2019061109

计算机应用 ›› 2020, Vol. 40 ›› Issue (1): 264-270.DOI: 10.11772/j.issn.1001-9081.2019061109

• 应用前沿、交叉与综合 • 上一篇下一篇

基于K近邻算法的噪声种类识别和强度估计

吴小莉, 郑艺峰

中国科学技术大学物理学院, 合肥 230000

收稿日期:2019-06-27 修回日期:2019-08-07 出版日期:2020-01-10 发布日期:2019-10-08
作者简介:吴小莉(1995-),女,江西赣州人,硕士研究生,主要研究方向:机器学习、噪声识别、信号处理;郑艺峰(1991-),男,江西鹰潭人,博士研究生,主要研究方向:等离子体数值计算、噪声研究、碰撞算法。
基金资助:
国家重点研发计划项目（2016YFA04006，2016YFA0400601，2016YFA0400602）；安徽省自然科学基金资助项目（1808085MA25）。

Noise type recognition and intensity estimation based on K-nearest neighbors algorithm

WU Xiaoli, ZHENG Yifeng

School of Physical Science, University of Science and Technology of China, Hefei Anhui 230000, China

Received:2019-06-27 Revised:2019-08-07 Online:2020-01-10 Published:2019-10-08
Contact: 郑艺峰
Supported by:
This work is partially supported by the National Key Research and Development Program of China (2016YFA0400600, 2016YFA0400601,2016YFA0400602),the Natural Science Foundation of Anhui Province (1808085MA25).

摘要/Abstract

摘要： 对于目前噪声种类识别和强度估计方法都是针对单噪声，无法估计混合噪声中源噪声的强度的问题，提出了一种有距离阈值的K近邻（KNN）算法，实现对单噪声和混合噪声的种类识别，并结合混合噪声识别结果和噪声基重构估计混合噪声中源噪声的强度。首先，选用频域数据分布作为特征向量；然后，采用噪声种类识别算法进行种类识别，并且在噪声基重构过程中以重构噪声与真实噪声的频域余弦距离作为强度估计算法的最优化评价标准；最后，实现对源噪声强度的估计。在两个测试数据库上的实验结果表明，所提算法的噪声种类识别的平均精度高达98.135%，混合噪声强度估计的误差率为20.96%。实验结果验证了噪声种类识别算法的准确性和泛化性，以及混合噪声强度估计算法的可行性，并且该方法为混合噪声强度估计提供了新思路。采用该方法获取的混合噪声种类和强度信息有助于去噪方法和去噪参数的确定，进而提高去噪效率。

关键词: K近邻算法, 距离阈值, 噪声基重构, 种类识别, 强度估计, 混合噪声

Abstract: For the problem that the existing methods for noise type recognition and intensity estimation all focus on single noises, and cannot estimate the intensity of source noises in the mixed noises, a K-Nearest Neighbors (KNN) algorithm with distance threshold was proposed to recognize the single and mixed noises, and estimate the intensity of source noises in the mixed noises by combining the recognition results of mixed noises and the reconstruction of noise bases. Firstly, the data distribution in frequency domain was used as feature vector. Then the signals were identified by the noise type recognition algorithm, and the frequency domain cosine distance between reconstructed noise and real noise was adopted as the optimal evaluation criterion in the process of reconstruction of noise bases. Finally, the intensity of source noises was estimated. The experimental results on two test databases indicate that, the proposed algorithm has the average accuracy of noise type identification as high as 98.135%, and the error rate of intensity estimation of mixed noise of 20.96%. The results verify the accuracy and generalization of noise type recognition algorithm as well as the feasibility of mixed noise intensity estimation algorithm, and this method provides a new idea for the mixed noise intensity estimation. The information of mixed noise type and intensity obtained by this method contributes to the determination of denoising methods and parameters, and improves the denoising efficiency.

Key words: K-Nearest Neighbors (KNN) algorithm, distance threshold, noise base reconstruction, type recognition, intensity estimation, mixed noise

中图分类号:

TP391.4

吴小莉, 郑艺峰. 基于K近邻算法的噪声种类识别和强度估计[J]. 计算机应用, 2020, 40(1): 264-270.

WU Xiaoli, ZHENG Yifeng. Noise type recognition and intensity estimation based on K-nearest neighbors algorithm[J]. Journal of Computer Applications, 2020, 40(1): 264-270.

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基于K近邻算法的噪声种类识别和强度估计

Noise type recognition and intensity estimation based on K-nearest neighbors algorithm

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