基于PUCS与DTCWT的红外与弱可见光图像融合

针对红外与弱可见光图像传统融合算法在结果图像中目标不突出、整体对比度降低、边缘及纹理细节不清晰、缺失等问题，本文提出一种基于感知一致性空间（Perception Unified Color Space，PUCS）和双树复小波变换（Dual Tree Complex Wavelet Transform，DTCWT）的融合算法。首先，将红外与弱可见光图像的亮度分量由RGB空间分别转至感知一致性空间得到新的亮度分量以备后续变换处理；接着，将源图像利用DTCWT进行多尺度分解，分别获取各自的低频分量与高频分量；然后，根据不同频带系数特点，提出一种基于区域能量自适应加权的规则对低频子带分量进行融合，采用一种基于拉普拉斯能量和与梯度值向量的规则对不同尺度、方向下高频子带分量进行融合；最后，对融合后的高、低频子带分量进行DTCWT逆变换重构图像，再将其转回至RGB空间以得到最终结果。在不同场景下将本文算法与3种高效融合算法进行对比评价，实验结果表明，本文算法不但在主观视觉上具有显著的目标特征、清晰的背景纹理及边缘细节、整体对比度适宜，而且在8项客观评价指标上也取得了较好的效果。

Infrared and Low-level Visible Light Images Fusion Based on Perception Unified Color Space and Dual Tree Complex Wavelet Transform

To solve problems in traditional image fusion, such as dim targets, low contrast, and loss of edge and textural details in fusion results, a new fusion approach for infrared and low-level visible light image fusion based on perception unified color space (PUCS) and dual tree complex wavelet transform (DTCWT) is proposed. First, the two-source image intensity component is separately transformed from RGB space into PUCS to obtain a new intensity component for further processing. Then, the infrared and low-level visible light images are decomposed using DTCWT to obtain the low- and high-frequency components, respectively. Subsequently, at the fusion stage, the region energy adaptive weighted method is adopted to fuse the low-frequency sub-bands, and the high-frequency rule uses the sum modified Laplacian and gradient value vector for different scale and directional sub-bands fusions. Finally, the fusion image is obtained by applying inverse DTCWT on the sub-bands and returned to RGB space. The proposed algorithm was compared with three efficient fusion methods in different scenarios. The experimental results show that this approach can achieve prominent target characteristics, clear background texture and edge details, and suitable contrast in subjective evaluations as well as advantages in eight objective indicator evaluations.