基于改进高低帽变换的钢轨裂纹红外图像增强

针对钢轨裂纹红外图像对比度低、信噪比低、纹理细节模糊而难以增强目标区域的问题,借助形态学高帽变换和低帽变换,提出了多尺度高帽低帽变换的钢轨裂纹红外图像增强优化算法。首先,用改进高帽变换、低帽变换分别提取多尺度明亮、暗淡图像区域；其次对多尺度的明亮与暗淡图像区域实施最大值的提取；然后操作其最大值以构建明亮和暗淡的图像区域；最后通过加权处理,实现图像增强。实验结果表明:本文算法在抑制噪声和突出了目标图像的边缘的基础上,有效地提高图像对比度,可应用于红外图像增强的场合,为后续图像信息处理奠定了必要的基础。

Infrared image enhancement of rail crack based on improved top-hat and bottom-hat transformation

As the infrared image of rail crack has low contrast,low signal-noise ratio,fuzzy texture detail and is difficult to enhance the target region,an infrared image enhancement optimization algorithm of rail crack based on the multi-scale top-hat and bottom-hat transformation is proposed. First,an improved top-hat transform is used to extract multi-scale bright image regions,and multi-scale dim image regions is extracted using an improved bottom-hat transform;then the bright and dark areas of the image are established through the maximum value extracted from the multi-scale bright and dim image regions;finally,image enhancement is realized through weighted processing. Experimental results indicate that the proposed algorithm can suppress the noise and highlight image edge of the target,meanwhile effectively improve the image contrast,which lays a necessary foundation for the further image processing.