目的 对带钢表面图像中的噪声进行有效滤除，以获取较清晰的带钢表面图像，为通过带钢表面图像检测带钢表面缺陷提供良好的源图。方法 利用非下采样 变换具有精细分解的特性，对获取到的带钢表面图像进行尺度分解，获取图像的高、低频系数。在最大后验概率框架的基础上，以噪声图像系数的方差为判断条件，构建了自适应去噪模型，对带钢图像进行前级去噪。为了进一步去除带钢表面图像中的噪声，利用非局部均值方法对前级去噪后的带钢表面图像进行处理，以达到对带钢表面图像中的噪声进行有效滤除。结果 仿真实验结果显示，与对照组方法相比，采用所设计方法去噪后的带钢表面图像不存在阶梯效应等不足，具有更高的峰值信噪比及结构相似度。结论 本文所提方法能有效去除带钢表面图像中夹杂的噪声，可获取较清晰的带钢表面图像。

The work aims to effectively filter noise in steel strip image and retain more image details, provide a good source figure for strip image-based surface defect detection. The strip image was decomposed to high and low frequency coefficients of the image as Nonsubsampled Contourlet Transformation (NSCT) had characteristics of fine decomposition. On the basis of maximum posterior probability, a self-adaptive threshold model was established for pre-denoising with variance of noise image coefficient. In order to further remove noise in steel strip image, the pre-denoised steel strip image was processed in the method of non-local means, and noise in the steel strip image was filtered effectively in this way. The simulation results showed that, compared with control group method, the proposed method was free from staircase effect and other shortcomings, and generated higher peak signal-to-noise ratio and structural similarity. The proposed method can effectively remove noise in the steel strip image, and can retain more image details, so that the denoised image has better visual effect.