低照度可见光与红外图像的自然感彩色融合能够显著提高人眼视觉在低照度环境下的情景感知和目标探测能力。基于样本的融合算法是一种快速有效、实时性强的自然感彩色融合算法。针对已有算法在查找表构建和灰度信息利用方面存在的问题,提出一种基于CbCr查找表的双波段图像彩色融合算法。算法采用反向传播神经网络对图像样本的二维亮度向量(Y₁,Y₂)和二维色度向量(C_b,C_r)进行非线性拟合,从而获得亮度与色度间的映射关系f(Y₁,Y₂)→(C_b,C_r),并由此构建CbCr查找表。融合时,由输入的双波段灰度图像的亮度Y₁,Y₂和CbCr查找表获得彩色融合图像的色度C_b,C_r;由亮度Y₁,Y₂经双层拉普拉斯金字塔融合获得彩色融合图像的亮度Y_F;为了减小因环境变化导致的色彩映射偏差,对亮度Y₁,Y₂进行灰度校正。实验结果表明,本文融合图像具有颜色自然、细节丰富、利于(热)目标检测的特点,在清晰度、彩色性、映射准确性方面已经达到甚至优于Toet算法的图像融合效果。

Natural color fusion of low-light visible images and infrared images can significantly improve abilities of human vision for situation perceiving and targets detecting in low-light environment. Sample-based color fusion is a fast, effective and real-time natural color fusion algorithm. In view of the problems of existing algorithms in construction of color look-up table and utilization of grayscale information, we propose a new color fusion algorithm of dual-band images based on CbCr look-up table. We obtain the mapping f(Y1,Y2)→(Cb,Cr) between luminance and chromaticity by using the back propagation neural network to nonlinearly fit the two-dimensional luminance vector (Y1,Y2) and the two-dimensional chromaticity vector (Cb,Cr) of image simples, and construct the CbCr look-up table based on the mapping. When color fusing, the chromaticity Cb,Cr of fused image are obtained by the CbCr look-up table and the input luminance Y1,Y2 of dual-band grayscale images. The luminance YF of fused image is obtained by the image fusion of luminance Y1,Y2 based on two-layer Laplacian pyramid transformation. The luminance Y1,Y2 are calibrated to diminish color mapping errors owing to environmental changes. The experimental results show that the fused images based on proposed algorithm have natural color, rich details and are more conducive to (hot) targets detection. The dual-band fusion results obtained by the proposed algorithm are almost as good as or even better than the fusion results by Toet method in definition, colorfulness, and mapping accuracy.