光学 精密工程, 2014, 22 (6): 1622, 网络出版: 2014-06-30   

齿轮视觉检测中的尺度与方向相关性联合降噪

Scale and directional correlation combined denoiseing in gear visual inspection
作者单位
中国计量学院 计量测试工程学院, 浙江 杭州 310018
摘要
建立了小模数齿轮视觉检测系统用于小模数齿轮的工业测量。针对该系统在标定过程对图像降噪的需求, 提出了基于Curvelet变换的域尺度与方向相关性联合降噪算法。首先, 介绍了Curvelet变换的原理和过程; 根据齿轮自身的对称性特点提出了利用尺度和方向间相关性的联合降噪算法, 并计算了对应的参数。然后, 比较了不同噪声情况下该算法的处理效果。最后, 同3种常见的降噪算法(高斯滤波, 小波降噪, 普通的Curvelet阈值降噪)进行了比较。结果显示: 经相关性联合降噪算法处理后的峰值信噪比(PSNR)优于高斯滤波6 db以上; 优于小波变换降噪3 db以上; 优于阈值降噪8 db以上。处理结果表明: 提出的算法可较好地保持齿轮边缘效果, 可满足小模数齿轮视觉检测系统后期摄像机自标定图像质量的需求。
Abstract
A visual inspection system for small modulus gears was established to realize their industry measurement. A de-noise algorithm based on scale-correlation and direction-correlation in Curvelet transform was proposed according to the de-noising demands in system calibration. First, the theory and process of the Curvelet transform were introduced. Then, according to the gear symmetry itself, a new combined algorithm for image de-noising based on Curvelet scale-correlation and direction-correlation was proposed, the corresponding parameters were calculated, and the processing results of the algorithm were compared under different noises. Finally, the algorithm was compared with 3 de-noise methods (Gaussian filter,Wavelet De-noise and Threshold De-noise). Experimental results indicate that the Peak Signal to Noise radio(PSNR) from the combined algorithm proposed is 6 db, 3 db and 8 db better than those of Gaussian filter, Wavelet De-noise and Threshold De-noise, respectively. It maintains good image edges and satisfies the image quality requirements of camera self-calibration in visual inspection systems for small modulus gears.

叶树亮, 张玉德, 张炜. 齿轮视觉检测中的尺度与方向相关性联合降噪[J]. 光学 精密工程, 2014, 22(6): 1622. YE Shu-liang, ZHANG Yu-de, ZHANG Wei. Scale and directional correlation combined denoiseing in gear visual inspection[J]. Optics and Precision Engineering, 2014, 22(6): 1622.

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