采用非局部均值和子带混合算法对图像进行去噪

Ehab Farouk Badran, Ammar Alhosainy
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引用次数: 1

摘要

本文提出了一种基于非局部均值算法和子带混合的图像去噪技术。采用两种不同的空间滤波参数对噪声图像进行两次非局部均值算法处理,然后对非局部均值算法得到的两幅图像分别进行离散小波变换(DWT)或轮廓波变换(CT)处理,然后进行子带混合,使峰值信噪比(PSNR)最大化。使用所提出的技术实现的改进约为0.65 dB。小波变换和轮廓波变换都被使用,它们的改进是相互接近的,哪一种更好取决于输入的噪声图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image denoising using non-local means algorithm and subbands mixing
In this paper, an image denoising technique using non-local means algorithm and subbands mixing is proposed. The non-local means algorithm is applied to the noisy image twice with two different spatial filtering parameters then either discrete wavelet transform (DWT) or contourlet transform (CT) is applied to the two resultant images of the non-local means algorithm, then a subbands mixing is preformed to maximize the peak signal to noise (PSNR). The improvement achieved using the proposed technique is about 0.65 dB. Both wavelet and contourlet transforms are used where their improvements are closed to each other and which of them is better varies according the input noisy image.
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