稀疏分解在钢水红外图像去噪中的应用

Sparse decomposition noise reduction in molten steel infrared image

基于辐射测温原理,介绍了红外测温理论和稀疏分解去噪原理,为了提高钢水的测温精度,对钢水的红外图像噪声进行处理。搭建钢水红外测温的实验平台,获得不同实验条件不同温度下的钢水红外图像,利用稀疏分解对钢水红外图像进行去噪处理。与维纳滤波图像去噪结果进行比较,实验结果表明,稀疏分解更好地去除了钢水红外热图像中的噪声,图像的峰值信噪比提高了10dB。

Based on the principle of radiation temperature measurement,we introduced the theory of infrared temperature measurement and sparse decomposition denoising principle in order to improve the molten steel temperature measuring accuracy and remove the noise of infrared image of molten steel.Having set up the experimental platform,we obtained different steel infrared images at different temperatures and experimental conditions,and used the sparse decomposition denoising for molten steel infrared image denoising processing.Compared with the Wiener filter denoising,the noise in the molten steel infrared image can be better removed by sparse decomposition and the peak signal to noise ratio increases by 10 dB.