基于k均值聚类的自适应小波图像去噪

Utkarsh Agrawal, S. Roy, U. Tiwary, D. Prashanth
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引用次数: 2

摘要

聚类算法通过将数据组织到几个聚类中来系统地检索数据。K-Means算法就是一种基于距离度量的无监督分组算法。聚类用于组织数据以实现高效检索。在本文中,我们研究了被分布在图像(数据集)上的可变高斯噪声损坏的图像的去噪。利用小波域的K-Means对训练图像的统计参数进行分组,得到数据集。对噪声图像进行自适应软阈值分割,根据聚类选择最佳参数。应用小波反变换计算去噪后图像的PSNR。应用这种技术获得了令人印象深刻的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
K-means clustering for adaptive wavelet based image denoising
Clustering algorithms are used for systematic retrieval of data by organizing them into several clusters. K-Means is one such algorithm which partitions data into groups based on distance metric in an unsupervised way. Clustering is used to organize data for efficient retrieval. In this paper, we study Denoising of images corrupted with variable Gaussian noise spread across the images (dataset). The dataset was made by applying K-Means grouping statistical parameters of the training images which are present in wavelet domain. Adaptive Soft thresholding of noisy images is done, selecting the best parameter based on the cluster. After applying inverse wavelet transform PSNR of the denoised image is calculated. Impressive results are obtained by applying this technique.
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