迭代自适应权重均值滤波的图像去噪

针对现有滤波方法滤除图像椒盐噪声的性能不理想和耗时长等缺陷，提出了一种迭代自适应权重均值滤波的图像去噪方法（IAWF）。首先，利用图像邻域像素与处理点的相似性采用新型方法构建邻域权重；然后，将此邻域权重与开关裁剪均值滤波结合形成新型权重均值滤波方法，充分利用像素间的相关性和开关裁剪滤波的优势，有效提高了算法的去噪效果，同时采用自适应的方式调整滤波窗口大小，以便尽可能地保护图像细节；最后，采用迭代式滤波方法，即如果上述操作还没有处理完噪声点，则迭代去噪直至噪声点处理完毕，实现自动处理。仿真实验结果表明，在各种不同噪声密度下，IAWF在峰值信噪比（PSNR）、失真度，以及视觉效果等方面均优于现有的几种优秀的滤波算法，且具有更快的运行速度，更适用于实际应用场合。

Iterative adaptive weighted-mean filter for image denoising

Aiming at the deficiencies of the current filters in removing salt-and-pepper noise from images, such as low denoising performance and slow running speed, an image denosing method based on Iterative Adaptive Weighted-mean Filter (IAWF) was proposed. Firstly, a new method was used to construct the neighborhood weight by using the similarity between the neighborhood pixels and the processed point. Then a new weighted-mean filter algorithm was formed by combing the neighborhood weight with switching trimmed mean filter, making full use of the correlation of the image pixels and the advantages of switching trimmed filter, effectively improving the denoising effect. At the same time, the window size of the filter was automatically adjusted to protect the details as much as possible. Finally, the iterative filter was applied to continue until the noisy points were processed completely in order to process automatically and reduce manual intervention. The simulation results show that compared with several state-of-the-art denoising algorithms, the proposed algorithm is better in Peak Signal-to-Noise Ratio (PSNR), collateral distortion and subjective denoising effect under various noise densities, with higher denoising speed, more suitable for practical applications.