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An optimal fuzzy filter for Gaussian noise in color images using bacterial foraging algorithm

机译:使用细菌觅食算法对彩色图像中高斯噪声的最佳模糊滤波器

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This paper presents an optimal fuzzy filter for Gaussian noise in color images using Bacterial Foraging Algorithm (BFA) and cosine similarity. The filter makes use of the relationship between different color components of a pixel to remove the noise from the color images. The adaptive cosine similarity between the central pixel and the neighboring pixels is estimated using color pairs red-green, red-blue and green-blue for noise removal. The membership function Large is defined and used to fuzzify similarity of each color component. Mean Square Error is used as an objective function for the bacterial foraging algorithm to learn the parameters of membership function Large. The correction term for the Gaussian filter is calculated using weighted average of the weights of all the neighboring pixels. The proposed Gaussian filter is found to be effective in eliminating noise from color images with the significant improvement in image quality. The experimental result on several color images proves the efficacy of the proposed fuzzy filter.
机译:本文介绍了使用细菌觅食算法(BFA)和余弦相似性的彩色图像中高斯噪声的最佳模糊过滤器。过滤器利用像素的不同颜色分量之间的关系来消除来自彩色图像的噪声。使用颜色对红绿色,红色蓝色和绿色用于噪声估计,估计中心像素和相邻像素之间的自适应余弦相似性。隶属度函数大型并用于模糊每个颜色组件的相似性。平均误差用作细菌觅食算法的目标函数,以了解隶属度函数的参数。使用所有相邻像素的权重的加权平均值计算高斯滤波器的校正项。发现所提出的高斯滤波器有效地消除来自彩色图像的噪声,具有图像质量的显着提高。几种彩色图像的实验结果证明了所提出的模糊过滤器的功效。

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