表面技术2017，Vol.46Issue(5)：249-254,6.DOI:10.16490/j.cnki.issn.1001-3660.2017.05.040
基于K-SVD算法的带钢表面缺陷图像去噪
Image Denoising of Strip Steel Surface Defects Based on K-SVD Algorithm
摘要
Abstract
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 typ-ical 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 recon-structed images of steel strip. Performance indices PSNR, MSE and SSIM are significantly better than other traditional filtering algorithms, and they have favorable denoising effects.关键词
K-SVD算法/正交匹配追踪/DCT字典/高斯噪声/滤波/带钢缺陷Key words
K-SVD algorithm/orthogonal matching pursuit/DCT dictionary/Gauss noise/filtering/strip defects分类
矿业与冶金引用本文复制引用
崔东艳,高蔚庭,夏克文..基于K-SVD算法的带钢表面缺陷图像去噪[J].表面技术,2017,46(5):249-254,6.基金项目
河北省自然科学基金(E2016202341) （E2016202341）
河北省引进留学人员基金(C2012003038) Suported by Hebei Province Natural Science Foundation (E2016202341), Hebei Province Foundation for Returned Scholars (C2012003038) （C2012003038）
