A Modified Wavelet Threshold Approach for Reducing Various Noise with Statistical Results

Saad Hussein Abed Hamed, Omar Khalid Salih Alhafidh, Y. S. Younis, Warif B. Yahia, Saleem Meften, Ali Hasan Ali
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Abstract

Different types of noise play a role in reducing the details of the images and blur the features which are important in many purposes. Physical and medical images are fractal in nature and especially the ones that are taken from experiments related to a medical field. This work investigates several noise reduction methods and reducing the fractional Brownian motion noise in physical and medical images using a modified wavelet threshold approach. The performance of this approach is analyzed and compared with some other approaches by using PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), and other criteria for the validity of the method.
基于统计结果的改进小波阈值降噪方法
不同类型的噪声在减少图像细节和模糊特征方面发挥作用,而这些特征在许多用途中都很重要。物理和医学图像在本质上是分形的,特别是从与医学领域相关的实验中获得的图像。本文研究了几种降噪方法,并使用改进的小波阈值方法降低物理和医学图像中的分数布朗运动噪声。通过峰值信噪比PSNR (Peak Signal to Noise Ratio)、均方误差MSE (Mean Square Error)等标准对该方法的有效性进行了分析,并与其他几种方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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