目的 有效滤除带钢表面缺陷图像高斯噪声。方法 高斯噪声是影响带钢图像质量的主要噪声类型之一，针对带钢表面缺陷图像高斯噪声去噪，首先对传统K-SVD（K-means and singular value decomposition）算法中的字典进行升级改造，然后采用正交匹配追踪（OMP，Orthogonal Matching Pursuit）算法对图像进行重构，滤除噪声，最后运用此算法对缺陷图像进行高斯滤波处理。为验证该算法去噪效果，选取几种常见的典型缺陷图像（划伤、气泡、氧化色、粘结纹）进行测试仿真，并选用中值滤波、均值滤波、小波变换、维纳滤波、3维块匹配（BM3D）等多种传统滤波方法进行比较。结果 该算法对四种典型缺陷去噪的PSNR（Peak Signal to Noise Ratio）值平均可达33.976 dB，MSE（Mean Square Error）平均值为27.607，SSIM（Structural Similarity）平均值为0.912。结论 该算法对带钢表面缺陷重构图像的边缘细节清晰，PSNR、MSE、SSIM三个性能指标明显优于其他传统滤波算法，去噪效果良好。

The work aims to effectively remove Gauss noise from surface defect image of strip steel. Gauss noise is one of the main types of noise affecting strip image quality. To remove Gauss noise from the surface defect image, firstly the dictionary of traditional K-SVD (K-means and Singular Value Decomposition) algorithm was improved, then orthogonal matching pursuit (OMP, Orthogonal Matching Pursuit) algorithm was used to reconstruct the image and remove the noise, later this algorithm was applied to Gauss noise filter of the defect image. In order to verify de-noising effect of the proposed algorithm, several typical defect images (scratches, bubbles, oxidation tint, bond lines) were selected for test simulation, and were compared in various traditional filtering methods including median filtering, mean filtering, wavelet transform, Wiener filter, 3D block matching (BM3D). In the proposed algorithm, average value of PSNR (Peak Signal to Noise Ratio) was 33.976 dB, MSE (Mean Square Error) 27.607 and SSIM (Structural Similarity) 0.912. This algorithm provides clear edges and details of surface defect reconstructed images of steel strip. Performance indices PSNR, MSE and SSIM are significantly better than other traditional filtering algorithms, and they have favorable denoising effects.