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- 2017
一种顾及区域特征差异的热红外与可见光图像多尺度融合方法
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Abstract:
针对传统的热红外与可见光图像融合方法对比度低，容易出现边缘细节、目标等信息丢失或减弱的现象，提出一种顾及区域特征差异的热红外与可见光图像多尺度融合方法。首先采用自适应PCNN（脉冲耦合神经网络）模型和二维Renyi熵相结合的图像分割方法，分别对红外和可见光图像进行区域分割；然后利用非下采样Contourlet变换对原图像进行多尺度多方向分解，根据区域的特征差异设计不同的融合规则，融合热红外与可见光图像。实验结果表明，该方法不仅能有效地融合热红外图像的目标特征，还能更多地保留可见光图像丰富的背景信息，融合图像对比度高，在视觉效果和客观评价上优于传统融合方法
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