|
|
|
| Image Denoising Method for Insulator Defect Detection Based on Adaptive BM3D |
| SHI Peiming,YUAN Qunmao,XU Xuefang,KAN Junming |
| College of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
|
|
|
|
Abstract Due to the influence of the shooting environment, the acquired image is often mixed with noise, which easily affects the accuracy of insulator defect detection. To solve this problem, an adaptive BM3D noise reduction method is proposed. First, a noise estimation algorithm based on the statistical relationship between the noise level and the eigenvalue of the image block covariance matrix is introduced to solve the problem that the original BM3D algorithm needs prior knowledge. Second, taking the peak signal to noise ratio (PSNR) as the objective function, the optimal parameters of the insulator image under each noise intensity are obtained by quantum genetic algorithm, including the hard threshold parameter and the distance threshold in the basic estimation, and the distance threshold in the final estimation. Finally, taking the noise intensity as the independent variable, the fitting curve of the above three parameters are obtained by polynomial fitting, so as to obtain the optimal parameters combination of the algorithm under each noise intensity, and realize the rapid parameter adaptation of BM3D algorithm under different noise levels. The results of comparative experiments indicate that the proposed method is superior to other methods in terms of visual and objective evaluation indexes. When the noise standard deviation is 25, the proposed method improves PSNR, structural similarity (SSIM) and edge preserve index (EPI) compared with the original BM3D algorithm, especially the EPI value increases by nearly 20%. While improving the noise reduction effect, it can retain more edge details, which is helpful to improve the effect of subsequent insulator identification and defect detection.
|
|
Received: 12 October 2023
Published: 05 September 2024
|
|
|
|
|
|
| ［4］ |
高一凡, 蔡静, 张学聪, 等. 基于NETD的红外热像仪图像预处理方法研究 ［J］. 计量学报, 2019, 40(6): 1020-1024.
|
| ［6］ |
陈宁, 郭钢祥, 郭斌, 等. 手机曲面玻璃缺陷检测方法研究 ［J］. 计量学报, 2023, 44(5): 701-706.
|
| ［12］ |
王洪斌, 王世豪, 籍冰朔, 等. 基于改进多阈值小波包的去噪算法及应用 ［J］. 计量学报, 2016, 37(2): 205-208.
|
| ［2］ |
胡毅, 刘凯, 吴田, 等. 输电线路运行安全影响因素分析及防治措施 ［J］. 高电压技术, 2014, 40(11): 3491-3499.
|
| ［13］ |
朱丹丹, 王斌, 杨奕, 等. Contourlet变换和遗传算法相结合的沥青红外图像增强方法 ［J］. 计量学报, 2019, 40(1): 25-30.
|
| ［18］ |
HASAN M, EL-SAKKA M R. Improved BM3D image denoising using SSIM-optimized Wiener filter ［J］. EURASIP Journal on Image and Video Processing, 2018(1):25.
|
| ［1］ |
宋新甫, 戴拥民, 赵志强, 等. 全球能源互联电网发展的规划技术框架及研究方向 ［J］. 电工技术, 2018 (9): 7-10.
|
| ［3］ |
SHAO L, YAN R M, LI X L, et al. From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms ［J］. IEEE Transactions on Cybernetics, 2014, 44(7): 1001-1013.
|
|
GAO Y F, CAI J, ZHANG X C, et al. Research on Image Preprocessing Method of Infrared Camera Based on NETD ［J］. Acta Metrologica Sinica, 2019, 40(6): 1020-1024.
|
| ［5］ |
吴健, 李洋, 韩义成, 等. 基于多尺度Retinex算法的杆塔多吊点约束绳系动点坐标计算方法 ［J］. 计量学报, 2023, 44(7): 1087-1092.
|
|
WU J, LI Y, HAN Y C, et al. Calculation Method of Moving Point Coordinates of Tower MultiLifting Point Constrained Rope System Based on Multi-Scale Retinex Algorithm ［J］. Acta Metrologica Sinica, 2023, 44(7): 1087-1092.
|
|
CHEN N, GUO G X, GUO B, et al. Study on Defects Detection Method of Mobile Phone Curved Glass ［J］. Acta Metrologica Sinica, 2023, 44(5): 701-706.
|
| ［8］ |
COUPE P, YGER P, PRIMA S, et al. An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images ［J］. IEEE Transactions on Medical Imaging, 2008, 27(4): 425-441.
|
| ［10］ |
CHANG S G, YU B, VETTERLI M. Adaptive wavelet thresholding for image denoising and compression ［J］. IEEE Transactions on Image Processing, 2000, 9(9): 1532-1546.
|
| ［11］ |
DA SILVA R D, MINETTO R, SCHWARTZ W R, et al. Adaptive edge-preserving image denoising using wavelet transforms ［J］. Pattern Analysis and Applications, 2013, 16(4): 567-580.
|
|
WANG H B, WANG S H, JI B S, et al. An Improved Multiple Threshold Wavelet Packet De-noising Algorithm and Its Application ［J］. Acta Metrologica Sinica, 2016, 37(2): 205-208.
|
| ［14］ |
PANKAJ D, NARAYANANKUTTY K A, GOVIND D. Image Denoising Using Total Variation Wavelet Galerkin Method ［C］// 8th International Conference on Advanced Computing & Communications. Kochi, INDIA, 2018.
|
| ［16］ |
DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering ［J］. IEEE Transactions on Image Processing, 2007, 16(8): 2080-2095
|
| ［19］ |
YANG D, SUN J. BM3D-Net: A Convolutional Neural Network for Transform-Domain Collaborative Filtering ［J］. IEEE Signal Processing Letters, 2018, 25(1): 55-59.
|
| ［21］ |
CHEN G Y, ZHU F Y, HENG P A. An Efficient Statistical Method for Image Noise Level Estimation ［C］// 2015 IEEE International Conference on Computer Vision. Santiago, Chile, 2015.
|
|
HU Y, LIU K, WU T, et al. Analysis of Influential Factors on Operation Safety of Transmission Line and Countermeasures ［J］. High Voltage Engineering, 2014, 40(11): 3491-3499.
|
| ［9］ |
THAIPANICH T, OH B T, WU P H, et al. Improved Image Denoising with Adaptive Nonlocal Means (ANL-Means) Algorithm ［J］. IEEE Transactions on Consumer Electronics, 2010, 56(4): 2623-2630.
|
| ［17］ |
MAKINEN Y, AZZARI L, FOI A. Collaborative Filtering of Correlated Noise: Exact Transform-Domain Variance for Improved Shrinkage and Patch Matching ［J］. IEEE Transactions on Image Processing, 2020, 29: 8339-8354.
|
|
SONG X F, DAI Y M, ZHAO Z Q, et al. Planning Technical Framework and Research Direction of Global Energy Interconnected Power Grid Development ［J］. Electric Engineering, 2018 (9): 7-10.
|
| ［7］ |
BUADES A, COLL B, MOREL J M. A non-local algorithm for image denoising ［C］// 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA. 2005.
|
| ［15］ |
MALLAT S G. A theory for multiresolution signal decomposition: the wavelet representation ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693
|
|
ZHU D D, WANG B, YANG Y, et al. Asphalt Infrared Image Enhancement of Combining Contourlet Transform with Genetic Algorithm ［J］. Acta Metrologica Sinica, 2019, 40(1): 25-30.
|
| ［20］ |
MA B, YAO J C, LE Y F, et al. Efficient image noise estimation based on skewness invariance and adaptive noise injection ［J］. IET Image Processing, 2020, 14(7): 1393-1401
|
|
|
|
2024, Vol. 45 
