摘要由于拍摄环境的影响,获取的图像中常常混有噪声,极易影响绝缘子缺陷检测的准确性。针对该问题,提出了一种自适应BM3D降噪方法。首先,引入基于噪声水平与图像块协方差矩阵特征值的统计关系的噪声估计算法,解决原始BM3D算法需要先验知识的问题;其次,以峰值信噪比(peak signal to noise ratio,PSNR)为目标函数,通过量子遗传算法得到绝缘子图像在各个噪声强度下的参数最优值,包括基础估计中的硬阈值参数、距离阈值和最终估计中的距离阈值;最后,以噪声强度为自变量,采用多项式拟合的方式得到上述3个参数的拟合曲线,从而得到各个噪声强度下算法的最佳参数组合,实现BM3D算法在不同噪声水平下的参数快速自适应。对比实验的结果表明所提出的方法在直观视觉和客观评价指标上优于其他方法。当噪声标准差为25时,所提出的方法相较于原始BM3D算法在峰值信噪比、结构相似性(structural similarity,SSIM)和边缘保留指数(edge preserve index,EPI)指标上均有所提升,尤其是边缘保留指数提升近20%。在提升降噪效果的同时能够保留更多边缘细节,这有助于提高后续绝缘子识别及缺陷检测的效果。
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.
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2024, Vol. 45 
