【作者】 朱寅； 吴敏； 曹卫华； 何勇；

【Author】 Zhu Yin,Wu Min,Cao Weihua,He Yong School of Information Science and Engineering,Central South University,Changsha 410083,P.R.China

【机构】 中南大学信息科学与工程学院；

【摘要】 针对高炉红外图像处理中存在图像噪声多、光源强度分布不均等问题,本文提出一种基于小波分解与图像增强的图像处理方法。首先利用Mallat快速小波分解算法,将原始高炉红外图像分解为高频分量与低频分量;然后利用改进的阈值滤波算法对高频分量进行滤波,同时采用Retinex算法对低频分量图像进行亮度调整;最后通过高频和低频图像的合成处理,得到纹理清晰的高质量高炉红外图像。本文提出的方法在某钢铁企业高炉红外图像处理中的应用,获得了较好的效果。更多还原

【Abstract】 In this paper,an image processing method based on wavelet decomposition and image enhancement is proposed to hand of the issues that there is much noise and uneven distribution of light source and light intensity,in blast furnace infrared images.Firstly,Mallat algorithm is employed to decompose blast furnace infrared images into wavelet details and lower frequency part.Then,improved threshold filtering algorithm is presented to process wavelet details,and Retinex Algorithm is used to adjust the intensity of the lower frequency part.Finally,high quality blast furnace infrared images with clear texture feature are obtained by imaging synthesis process of wavelet details and lower frequency part.As a result,the presented method is applied to the process of blast furnace infrared images in some iron enterprise and the efficiency and merits are shown.更多还原