A New Non-local Mean Method for the Image Denoising of Coal Dust

6th Annual 2018 International Conference on Geo-Spatial Knowledge and Intelligence

Miao, Chuanjie^1 ; Liang, Chao^1

School of Computer Science and Engineering, Changchun University of Technology, Changchun

130012, China^1

关键词: Coal dust images;    Cosine coefficient;    De-noising algorithm;    Filtering algorithm;    Gaussian kernel functions;    Image denoising algorithm;    Non local means;    Weighting coefficient;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/234/1/012090/pdf
DOI  :  10.1088/1755-1315/234/1/012090

来源: IOP

【 摘 要 】

In the process of coal dust image collecting and transmitting, it is inevitable that it will be interfered by noise. The denoising effect of image is crucial for the segmentation and recognition of image behind. The denoising effect of image is better based on the non-local mean denoising algorithm. However, the exponential weighted kernel function is used in the traditional non-local mean filtering algorithm, which tends to cause the image details to be blurred due to excessive smoothing. Therefore, according to the exponential weighted kernel function, a new non-local mean image denoising algorithm is designed by using the Gaussian kernel function of weighted of cosine coefficient, and applied to the weighting coefficient calculation. The experimental results show that the denoising performance of the algorithm is better than the traditional algorithm and can better preserve the details of coal dust image.