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Fast adaptive and selective mean filter for the removal of high-density salt and pepper noise

机译:快速自适应和选择性均值滤波器,用于去除高密度盐和胡椒噪声

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摘要

A fast adaptive and selective mean filter is presented to remove salt and pepper noise effectively from images corrupted with higher noise densities. The algorithm achieves better results in terms of visual quality and in terms of peak signal-to-noise ratio, mean absolute error, mean structural similarity index measure, image enhancement factor, and edge preservation ratio than many existing state-of-the-art algorithms at all noise densities. Adaptive filters that use variable window size produce better restoration of salt and pepper noise at higher noise densities than filters that use fixed window size, but they consume more time. This makes them practically impossible to implement them in digital image acquisition devices. Hence, reducing the execution time of adaptive filters is vital. The proposed algorithm consumes around 90% less time for lower noise densities and 50% less time for higher noise densities than the adaptive weighted mean filter, one of the best available adaptive filters in the literature for high-density salt and pepper noise removal.
机译:提出了一种快速自适应和选择性均值滤波器,可从具有较高噪声密度的图像中有效去除盐和胡椒噪声。与许多现有技术相比,该算法在视觉质量,峰值信噪比,平均绝对误差,平均结构相似性指标度量,图像增强因子和边缘保留率方面都取得了更好的结果所有噪声密度的算法。与使用固定窗口大小的滤波器相比,使用可变窗口大小的自适应滤波器可以在更高的噪声密度下更好地恢复盐和胡椒噪声。这使得它们实际上不可能在数字图像采集设备中实现。因此,减少自适应滤波器的执行时间至关重要。与自适应加权平均滤波器相比,拟议算法与较低的噪声密度相比,消耗的时间减少了约90%,而对于较高的噪声密度,则消耗了约50%的时间。

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