光照分量自适应校正的古城墙表面病害图像信息增强方法

Information Enhancement Method for Surface Disease Images of Ancient City Walls Based on Adaptive Correction of Illumination Component

针对太阳光照变化、物体间遮挡、拍摄角度偏差等干扰造成古城墙表面病害图像信息被隐藏,病害特征提取与识别效果不佳的问题,提出了一种基于光照分量自适应校正的古城墙表面病害图像信息增强方法。利用多尺度高斯函数连续卷积提取光照分量,根据像素点光照分量与均值的偏差非线性变换关系确定Gamma参数,构造增强2D-Gamma函数自适应校正图像过亮和偏暗区域的亮度值,恢复隐藏信息;同时为解决2D-Gamma函数对高亮像素点亮度值衰减表征能力较弱问题,使用同态滤波在频域压制高亮区域的光照分量,减小亮度动态范围,增强图像高亮区域隐藏信息;并利用系数因子将亮度校正后图像与Sobel算子提取的边缘细节图像线性融合,重构获取细节清晰、纹理突出的古城墙表面病害增强图像。实验结果表明,对于受光照、遮挡等干扰因素影响可视性较差的图像,所提方法在保留原图像细节信息的同时有效解决了光照不均、遮挡和阴影等造成的图像信息隐藏问题,增强了古城墙表面病害图像特征,提高了病害识别的准确率。

In this paper,we propose an information enhancement method for surface disease images of ancient city walls based on adaptive correction of the illumination component to solve the problems of hidden information of surface disease images of ancient city walls and poor disease feature extraction and recognition due to the interference of sunlight change,occlusion between objects,and shooting angle deviation.The light component was extracted using continuous convolution in a multiscale Gaussian function,and the Gamma parameters were determined according to the nonlinear transformation relationship between the deviation of the light component and the mean value of the pixel.Next,an enhanced twodimensional(2D)-Gamma function was constructed to adaptively correct the luminance values of the overbright and dark areas of the image to recover the hidden information.At the same time,homomorphic filtering was used to suppress the illumination components of the highlighted areas in the frequency domain in order to solve the problem of weakness of the 2D-Gamma function in characterizing the attenuation of the brightness values of highlighted pixels.This reduces the dynamic range of luminance and enhances the hidden information of highlighted areas of the image.A coefficient factor was used to linearly fuse the brightness-corrected image with the edge details extracted using the Sobel operator to reconstruct an enhanced surface disease image of the ancient city walls with clear details and prominent textures.The experimental results show that the proposed method effectively solves the problem of hidden image information and poor visibility caused by uneven illumination,occlusion,and shadows while retaining the original image details.Moreover,it enhances the surface disease image features of the ancient city walls and improves the accuracy of disease recognition.