一种变换域无线多媒体传感器网络图像去噪方法的对比分析

R. Dhaya
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摘要

近年来,由于对稀疏表示的重视,图像去噪的研究越来越受到关注。将稀疏表示理论与基于变换域的图像去噪进行比较,前者表明图像具有更多的信息。它包含的结构特征与基于字典的原子的结构非常相似。这种结构和基于字典的方法是非常不成功的。然而,图像表示假设噪声缺乏这样的特征。双树复小波变换增加了变换数据密度,减少了稀疏数据的影响。该技术通过选择由小波系数导出的最佳预测阈值来降低图像噪声。在我们的实验中,使用离散余弦变换(DCT)和复小波变换(CWT)来检查所建议的技术如何在真实图像集上比较传统DCT和CWT。对于图像质量度量,DT-CWT利用了优越的结果。在处理时间方面,DT-CWT在更宽的PSNR范围内获得了更好的结果。此外,用标准数字图像Lena和多媒体传感器图像对该模型进行了去噪算法测试。建议的去噪技术对MSE值的影响最小。
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Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
In recent years, there has been an increasing research interest in image de-noising due to an emphasis on sparse representation. When sparse representation theory is compared to transform domain-based image de-noising, the former indicates that the images have more information. It contains structural characteristics that are quite similar to the structure of dictionary-based atoms. This structure and the dictionary-based method is highly unsuccessful. However, image representation assumes that the noise lack such a feature. The dual-tree complex wavelet transform incorporates an increase in transform data density to reduce the effects of sparse data. This technique has been developed to decrease the image noise by selecting the best-predicted threshold value derived from wavelet coefficients. For our experiment, Discrete Cosine Transform (DCT) and Complex Wavelet Transform (CWT) are used to examine how the suggested technique compares the conventional DCT and CWT on sets of realistic images. As for image quality measures, DT-CWT has leveraged superior results. In terms of processing time, DT-CWT gave better results with a wider PSNR range. Further, the proposed model is tested with a standard digital image named Lena and multimedia sensor images for the denoising algorithm. The suggested denoising technique has delivered minimal effect on the MSE value.
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