针对目前红外与可见光图像融合速度慢、 融合结果对比度不高且易产生伪影的缺点, 提出一种基于Tetrolet变换的改进融合算法。 首先, 将可见光图像转换到lαβ颜色空间得到三个几乎不相关的彩色通道; 然后对其l分量和红外图像分别进行Tetrolet变换, 对于低通系数引入邻域能量及其接近度的融合规则。 而对Tetrolet系数采用伪随机傅里叶矩阵进行观测并加权融合其观测值; 接下来对融合后观测值采用CoSaMP优化算法迭代出融合后的Tetrolet系数, 并经Tetrolet重构得到融合后的灰度图像; 最后将灰度图像映射到RGB颜色空间获得最终的融合图像。 实验证明了本文算法的有效性。

The present study an improved fusion algorithm was proposed based on the Tetrolet transform. It was used to solve the problems that the infrared and visible light images fusion speed is slow, the contrast of the fused image is low and it is easy to bring artifacts to the fused image. First of all, the visible light image was converted to the lαβ color space to get three irrelevant color channels. Secondly, the component l and infrared image were decomposed by the Tetrolet transform. The neighborhood energy and proximity were introduced to the low-pass coefficients fusion rule. The Tetrolet coefficients were observed by the pseudo-random Fourier matrix. The observation value was weightedly fused. Thirdly, the fused observation value were iterated by the CoSaMP optimization algorithm to get the fused Tetrolet coefficient. The fused gray image was got after the Tetrolet reconstruction. Finally, the final fused image was obtained by mapping the grey image to the RGB color space. The experiment results testified the algorithm validity for the image fusion.