改进同态滤波与多尺度融合的腐蚀图像增强

Improved homomorphic filtering and multiscale fusion for corroded image enhancement

为了能够更好地判断金属腐蚀图像的腐蚀程度,针对腐蚀图像存在的亮度不高、对比度低和细节模糊等问题,提出一种改进的同态滤波与多尺度融合的腐蚀图像增强方法。首先,采用引导滤波将原始腐蚀图像分为基础图像和细节图像后加权融合,获得细节对比度增强图像;其次将原始腐蚀图像转换为HSV颜色空间,对亮度分量采用改进后的单参数分块同态滤波得到亮度增强图像,能够在减少同态滤波参数的同时,改善同态滤波亮度过度增强的现象;最后利用拉普拉斯对比度、显著性和饱和度3个权重对处理后具有优势特征的两幅图像进行多尺度融合,得到最终的增强图像。实验结果表明,本文算法的信息熵、均值、平均梯度以及标准差的平均值相较于原图分别提升了7.4%、9.8%、43.34%和29.8%,其中信息熵、平均梯度以及标准差的平均值均优于其余3种算法;本文算法能有效改善腐蚀图像整体亮度,提升暗细节对比度,提高图像质量。

In order to better judge the corrosion degree of metal corrosion images,in view of the problems of low brightness,low contrast and blurred details in corrosion images,an improved corrosion image enhancement method based on homomorphic filtering and multi-scale fusion is proposed.First,the original corroded image is divided into the base image and the detail image by guided filtering and then weighted and fused to obtain the detail contrast-enhanced image.Secondly,the original eroded image is converted into HSV color space,and only the luminance component is subjected to the improved single-parameter block homomorphic filtering to obtain a luminance-enhanced image,which can reduce the homomorphic filtering parameters and improve the phenomenon of excessive brightness enhancement of homomorphic filtering.Finally,using three weights of Laplacian contrast,saliency and saturation to perform multiscale fusion on the two images with dominant features after processing to obtain the final enhanced image.The experimental results show that the information entropy,mean,average gradient and standard deviation of the algorithm in this paper are improved by 7.4%,9.8%,43.34%and 29.8%respectively compared with the original image.Among them,the average value of information entropy,average gradient and standard deviation are better than the other three algorithms.The algorithm in this paper can effectively improve the overall brightness of the corroded image,improve the contrast of dark details,and improve the image quality.