基于非局部均值滤波的结构光图像去噪  

Structured Light Image Denoising Based on Non-local Mean Filtering

作　　者：汪平 李锋[1] WANG Ping;LI Feng(School of Electronic Information,Jiangsu University of Science and Technology,Zhenjiang 212000)

机构地区：[1]江苏科技大学电子信息学院,镇江212000

出　　处：《计算机与数字工程》2024年第4期1191-1195,共5页Computer & Digital Engineering

摘　　要：采用一种改进的自适应非局部均值滤波结合小波去噪的方法对结构光图像进行滤波处理。首先针对椒盐噪声,采用一种有效的自适应检测窗口确认噪声点,噪声像素被其相邻的三个像素的组合所取代,然后使用基于椒盐噪声特征的非局部均值滤波来重构噪声点的灰度值。进一步地针对高斯噪声,对图像进行小波变换,并对高频部分进行方向性中值滤波,最终经小波逆变换进行重构,得到去噪后的结构光条纹图像。实验结果显示,与传统方法进行比较,改进的方法能够有效地滤除结构光图像中的噪声,并保持了结构光条纹图像的细节信息。An improved adaptive nonlocal mean filtering method combined with wavelet transform denoising is used to prepro-cess structured light images.Firstly,an effective adaptive detection window is used to confirm the noise points,and the noise pixels are replaced by the combination of three adjacent pixels.Then,the gray value of the noise points is reconstructed by non-local mean filtering based on the features of salt and pepper noise.Furthermore,the image is transformed by wavelet,and the high frequency component is filtered by directional median,and the denoised image is reconstructed by inverse wavelet transform.The experimen-tal results show that compared with the traditional method,the improved method can effectively filter out the noise in the structured light image and keep the details of the structured light fringe image.

关 键 词：结构光图像 非局部均值滤波 噪声检测 小波变换 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]