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Image denoising using non-local means algorithm and subbands mixing

机译:使用非局部均值算法和子带混合的图像去噪

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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.
机译:本文提出了一种使用非局部均值算法和子带混合的图像去噪技术。将非局部均值算法通过两个不同的空间滤波参数两次应用于噪声图像,然后将离散小波变换(DWT)或轮廓波变换(CT)应用于非局部均值算法的两个结果图像,然后进行子带混频以使峰值信噪比(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在改进效果彼此接近的情况下使用,其中哪一个更好,则根据输入的噪点图像而有所不同。

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