针对中值滤波导致部分图像细节损失和均值滤波出现模糊现象,设计了一种适用于椒盐和高斯混合噪声的自适应滤波算法。该算法先用最小邻域的均值和阈值判断噪声类型,然后使用加权中值滤波处理椒盐噪声,再利用拉普拉斯算子和相应阈值判断图像边缘细节,最后对高斯噪声进行加权均值滤波。实验仿真结果表明,从图像视觉效果来看,相比单独使用中值和均值滤波降噪,自适应滤波算法对图像的还原效果更好,图像细节保存较好,模糊程度相对较弱,图像更清晰。通过对比峰值信噪比(PSNR)和均方误差(MSE),对混合噪声进行处理时,滤波算法的PSNR和MSE值优于中值和均值滤波,有效还原了噪声图像。整个算法是在最小邻域空间进行,易于实现,对混合噪声的处理效果较好,为图像处理的系统集成化设计提供了技术支持。 Aiming at the vagueness of detail loss and mean filtering of median image caused by median filtering, an adaptive filtering algorithm suitable for mixed salt and salt and Gaussian noise was designed. The algorithm first uses the mean and threshold of the smallest neighborhood to judge the type of noise. Then the weighted median filter is used to process the salt-and-pepper noise. The Laplacian operator and the corresponding threshold are used to judge the edge details of the image. Finally, the Gaussian noise is weighted averagely filtered. The experimental results show that, compared with the median and mean filtering denoising alone, the adaptive filtering algorithm is better for image restoration, image detail preservation is better, the blurring degree is relatively weaker, the image is more Clear. By comparing the PSNR and MSE, the PSNR and MSE of the filtering algorithm are better than the median and mean filter when the mixed noise is processed, and the noise image is effectively restored. The whole algorithm is carried out in the minimum neighborhood space and is easy to implement. The hybrid noise processing is effective and provides technical support for the system integration design of image processing.