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A novel algorithm of image denoising and edge preserving based on discrete grey model

机译:一种基于离散灰色模型的图像去噪和边缘保存的新算法

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摘要

The filtering method of image with salt & pepper noise is presented in this paper, which is based on discrete grey model (DGM). First, using four directional convolution operators detect noise point and noise-free point. In evidence, the threshold affects the performance of noise detection. We can determine a reasonable threshold using a genetic algorithm to minimize the mean square error of the filtered image in the paper. DGM is used to process this noise point, which uses a pixel grey value of eight points around the noise point of image to build up a discrete grey model, and the first forecasting value replaces the original pixel grey value of the noise point to realize the forecast filter, and noise-free points are not processed. Comparison experiments between the proposed algorithm and three other methods are carried out under the different noise level of image. Experimental results show that the proposed algorithm has better processing effect for images with salt & pepper noise, where the filter effect has been evaluated by using the mean square error and peak signal to noise ratio objectively.
机译:本文提出了具有盐和辣椒噪声的图像的过滤方法,其基于离散灰度模型(DGM)。首先,使用四个方向卷积运营商检测噪声点和无噪声点。在证据中,阈值会影响噪声检测的性能。我们可以使用遗传算法确定合理的阈值,以最小化纸张中滤波图像的平均误差。 DGM用于处理该噪声点,它在图像的噪声点周围使用八个点的像素灰度值来构建一个离散的灰度模型,并且第一预测值替换噪声点的原始像素灰度值以实现预测过滤器,没有处理无噪声点。在图像的不同噪声水平下进行了所提出的算法和三种其他方法之间的比较实验。实验结果表明,该算法对具有盐和辣椒噪声的图像具有更好的处理效果,其中通过使用平均方误差和峰值信号客观地评估滤波器效果。

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