摘要
目的图像复原是基于物理模型提高退化图像质量的一种客观方法,复原图像无失真且细节丰富。烧结机尾断面火焰图像可以反映料层的烧结状态,对烧结矿质量的检测起到至关重要的作用。由于烧结机尾环境恶劣,存在大量的烟气、粉尘以及亮度不均等干扰因素,导致相机采集到的烧结断面火焰图像存在退化现象。为消除这些影响,本文建立了烧结断面火焰图像退化模型,提出了有效的烧结断面火焰图像复原算法。方法基于大气散射模型,采用一级多散射方法对烟尘多次散射过程进行简化,建立烧结断面火焰图像退化模型,依据Retinex理论,将场景成像分解为环境光照射分量与反射率的乘积,明确复原图像所求参数。1)求取原始图像亮度,利用Retinex理论分解原始图像,使用双边滤波来调整亮度图像,采用Sigmoid函数对反射图像进行增强,得到亮度平衡后新的烧结断面火焰图像;2)利用暗通道原理估计环境光值,结合引导滤波细化图像透射率分布;3)采用容差机制改进火焰区域的透射率,得到复原图像。结果使用本文方法对单幅图像进行复原并与其他4种方法进行主客观评价,结果表明本文得到的复原图像亮度均衡,火焰区域细节清晰并且与烧结料层区别明显,在保持较高图像对比度的同时,图像信息熵和峰值信噪比分别为17.532 bit与22.127 dB,相比其他算法明显提高。结论本文研究了烧结断面火焰图像的退化模型,提出有效的复原算法,实现了Retinex理论与暗通道原理的有机结合,复原图像质量较高,为烧结火焰特征准确提取打下基础。
Objective Compared with traditional subjective image enhancement methods,image restoration is an objective method to improve the quality of degraded images on the basis of physical models.The result presents no distortion,low noise,and rich image details.The grade of sinter,which is the main raw material for blast furnace ironmaking,is directly related to the condition of the blast furnace and the output of molten iron.The flame image of the tail section of a sinter machine can fully reflect the sintering state of the material layer,which plays an important role in the detection of the quality of a sintered ore.However,the harsh environment of the sintering machine tail has caused numerous interference factors,such as smoke,dust,and uneven brightness.Such factors lead to the degradation of the flame image of the sintered section collected using a camera.An accurate ambient light value is difficult to obtain by using the dark channel principle alone.This limitation results in halo and distortion.Therefore,obtaining a clear and undistorted cross-section flame image of the tailing layer of the sintering machine is the primary problem in accurately identifying the end point of sintering,and it has an important engineering application value for improving the yield of sintered ore and reducing energy consumption.In this study,a new flame image degradation model for the sintered section is established,and an effective flame image restoration algorithm for the sintered section is proposed.Method The basis of the image restoration algorithm based on physical model is to establish a degradation model of the image.The atmospheric scattering model proposed by Narasimhan is widely used in the fields of computer vision and image processing.The image degradation model is established based on the atmospheric scattering model;it fully considers the scattering and attenuation characteristics of soot particles in the tail environment of the sintering machine.The model uses a first-order multiscattering method to simplify the multiscattering process of soot and Retinex theory to decompose the image.First,the proposed algorithm calculates the brightness of the original image and adjusts the overall brightness of the image based on the bilateral filtering Retinex method in accordance with the uneven luminance caused by the sintering flame.Second,from the results of image brightness balance,we estimate the ambient light of the sintering machine tail by using the transcendental principle of dark channel and enlarge the image transmissivity distribution with guided filtering.Finally,for the image of the area containing a large region of flame in the smoke environment of the sintering section,we use the tolerance mechanism to improve the transmittance of the flame area and acquire a restored image.This process prevents the color distortion of the restored image and makes the restored image close to the actual situation.Result In this experiment,a single image collected is used for restoration and subjective evaluation with four other experimental methods.Results of multiscale Retinex restoration show that the method cannot remove the smoke and dust from the flame image of the sintering section,and the noise is excessively high.Compared with the results of multiscale Retinex,the image noise generated using the method with glow and multiple light colors is smaller,but the details of the flame area are unclear and color distortion appears.The results of dark channel priori method experimental restoration improve some details of the flame area but do not eliminate the influence of soot because the flame area causes uneven brightness of the image.This condition leads to the failure of the dark channel priori to estimate the transmission of the image in the dark scene.The experimental results of the method with boundary constraint and contextual regularization present that the image brightness is balanced in a small range,and the details of the flame area are obvious.However,the overall image brightness is high,and the smoke influence remains.The results of this study show that the brightness of the restored image is balanced,the details of the flame area are clear and distinct from the sintering layer,the color is natural without distortion,and the influences of smoke and dust are eliminated.We use a statistical method for image quality evaluation,pixel value variance,average gradient,contrast,information entropy,and peak signal-to-noise ratio as objective evaluation indexes.The experimental results of dark channel priori method have lower statistics than the original image because the method does not address the problem of uneven brightness of the image caused by the sintering flame area.Compared with the statistics of the original image,the statistics of the image obtained using modified method is improved,but the restored image has the problems of noise and color distortion.The algorithm proposed in this study is superior to other methods.The algorithm results maintain a high image contrast.The image information entropy and the peak signal-to-noise ratio are 17.532 bit and 22.127 dB,respectively,which are significantly higher than those of other algorithms.Conclusion We study a degradation model for the flame image of the tail section of a sintering machine and propose an effective restoration algorithm.We achieve the brightness balance of the restored image and the effect of smoke removal,which are beneficial to the accurate extraction of sintered flame characteristics and lay a foundation for the subsequent identification of the sintering end point.
作者
王福斌
刘贺飞
武晨
Wang Fubin;Liu Hefei;Wu Chen(College of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,China;Tang Steel International Engineering Technology Corp,Tangshan 063000,China)
出处
《中国图象图形学报》
CSCD
北大核心
2020年第7期1356-1365,共10页
Journal of Image and Graphics
基金
高端钢铁联合研究基金资助项目(F2019209323)。
