一种改进的各向异性扩散去噪算法

An Improved Algorithm for Anisotropic Diffusion Smoothing Noise

平滑图像中的噪声是数字图像处理中非常重要的组成部分。在图像处理过程中,为了有效地实现保边缘平滑,在各向异性扩散模型和含有噪声的图像数据统计特性的基础上,提出了一个能自适应地获取参数的各向异性扩散去噪模型。该模型针对不同程度的噪声图像采用不同的参数值。实验结果表明,改进后的各向异性扩散模型的性能优于Perona-Malik模型,是一种较为理想的保边缘平滑模型。

Smoothing noise plays a very important role in digital image processing.The anisotropic diffusion is a selective smoothing technique that effectively employs intra-region smoothing without limitation and inhibits inter-region smoothing.In order to remove noise and preserve edges effectively,a more effective and adaptive diffusion algorithm is proposed in this paper.The algorithm is based on anisotropic diffusion model and the statistical properties of image with noise.The value of parameter can be adaptively selected by the proposed algorithm based on the different signal-to-noise ratio.It has been shown from the experiments that the improved scheme has superiority capability over the Perona-Malik scheme and it is a robust anisotropic diffusion.