石坤泉,魏文国.采用双边滤波的冷轧铝板表面缺陷图像去噪方法的研究[J].表面技术,2018,47(9):317-323.
SHI Kun-quan,WEI Wen-guo.Image Denoising Method of Surface Defect on Cold Rolled Aluminum Sheet by Bilateral Filtering[J].Surface Technology,2018,47(9):317-323
采用双边滤波的冷轧铝板表面缺陷图像去噪方法的研究
Image Denoising Method of Surface Defect on Cold Rolled Aluminum Sheet by Bilateral Filtering
投稿时间:2018-04-24  修订日期:2018-09-20
DOI:10.16490/j.cnki.issn.1001-3660.2018.09.042
中文关键词:  图像去噪  冷轧铝板表面缺陷图像  双边滤波  概率分布函数  最大似然函数  区域相似度模型
英文关键词:image denoising  surface defect image of cold rolled aluminum sheet  bilateral filtering  probability distribution function  maximum likelihood function  regional similarity model
基金项目:广州市科技计划项目(201806040010,201802020019)
作者单位
石坤泉 1.广州番禺职业技术学院 信息工程学院,广州 511483 
魏文国 2.广东技术师范学院 电子与信息工程学院,广州 510665 
AuthorInstitution
SHI Kun-quan 1.School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou 511483, China 
WEI Wen-guo 2.School of Electronics and information, Guangdong Polytechnic Normal University, Guangzhou 510665, China 
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中文摘要:
      目的 去除冷轧铝板表面缺陷图像中的噪声,并保持图像的清晰度以及图像的细节信息,避免冷轧铝板表面缺陷图像中的噪声引起错误的缺陷检测。方法 首先,引入双边滤波方法,并联合概率分布函数以及最大似然函数求取图像的噪声方差,自适应地对双边滤波函数中灰度方差值进行调整,实现对冷轧铝板表面缺陷图像中噪声进行滤除。然后,为了对双边滤波去噪后遗留下的强噪声进行去除,利用像素点之间的差值,构造区域相似度模型,对双边滤波去噪后图像中的强噪声进行判定。最后,借助中值滤波方法在对强噪声进行滤除的同时,兼顾保持图像的清晰度,进而达到去除冷轧铝板表面缺陷图像中的噪声,并保持图像细节以及清晰度的目的。结果 所设计方法在噪声强度分别为0.09、0.12以及0.15时,所得去噪图像的MSE值分别为15.3743、19.7713以及23.7613,所得去噪图像的PSNR值分别为38.4971、35.4792以及31.1768。结论 所设计方法不仅能有效去除冷轧铝板表面缺陷图像中的噪声,而且还能较好地保持图像的清晰度以及边缘特征,使得去噪后图像具有较好的视觉效果。
英文摘要:
      The work aims to remove noise in surface defect image of cold rolled aluminum sheet, keep resolution and details of the image, and avoid incorrect defect detection due to noise in the surface defect image of cold rolled aluminum sheet. Firstly, noise variance of the image was obtained by introducing a bilateral filtering method, and combining probability distribution function and the maximum likelihood function, and gray variance value in the bilateral filter function was adjusted to filter the noise in the surface defect image of cold rolled aluminum sheet. Then, in order to remove strong noise left after the bilateral filtering denoising, regional similarity model was constructed based upon the difference between pixels, and the strong noise in the image after denoising was determined by the bilateral filtering. Finally, the median filtering method was used to filter the strong noise while maintaining solution of the image, so as to further remove noise in the surface defect image of cold rolled aluminum sheet and keep the details and resolution of the images. In the method proposed herein, MSE value of the denoised images was 15.3743, 19.7713 and 23.7613, respectively, and PSNR value was 38.4971, 35.4792 and 31.1768, respectively when noise intensity was 0.09, 0.12 and 0.15, respectively. The proposed method can not only effectively remove the noise in the surface defect image of the cold rolled aluminum sheet, but also maintain image resolution and edge features satisfactorily, so that the denoised image has good visual effect.
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