结合小波变换和双边滤波的SICM图像降噪算法

扫描离子电导显微镜(Scanning Ion Conductance Microscope, SICM)能够实现微纳米级的形貌测量，引起广大学者的关注研究。针对SICM形貌图像易受噪声污染，影响后续应用的问题，提出了一种基于小波分层阈值处理的双边滤波算法。针对SICM形貌图像的多特征融合噪声，采用伪中值滤波局部处理图像中的强斑点噪声，有机结合小波阈值去噪和双边滤波去除图像高频和低频噪声，最终得到去噪效果较好的形貌图像。通过仿真实验和实测实验进行多次验证，本文算法对比中值滤波、双边滤波和小波去噪三种去噪算法，其峰值信噪比提升幅度均大于9.8%。实验表明本文算法在SICM形貌图像去噪方面具有更大优势。

A denoising algorithm for SICM image based on wavelet and bilateral filtering

Scanning ion conductivity microscopy (SICM) is capable of achieving micron-scale and nanometer-scale morphology measurements, which has attracted the attention of scholars for research. A bilateral filtering algorithm based on wavelet hierarchical thresholding is proposed to solve the problem that SICM morphology images are vulnerable to contamination, which can affect subsequent applications. For the multi-feature fusion noise of SICM morphological image, pseudo-median filtering is applied to partly process the strong speckle noise in the image, and wavelet threshold denoising and bilateral filtering are organically combined to remove the high-frequency and low-frequency noise of the image. Finally, a morphological image with good denoising effect is obtained. The algorithm is verified several times through the simulation experiment and the real test experiment, and its peak signal-to-noise ratio improvement is greater than 9.8% when comparing the three denoising algorithms of median filtering, bilateral filtering and wavelet denoising. The experimental results show that this algorithm has more advantages in the denoising of SICM morphological images.