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.