为提高融合效率, 解决基于多尺度变换的融合方法中融合系数选择错误的问题, 提出一种红外与可见光图像融合方法.首先用非下采样双树复轮廓波变换将源图像分解为低频与高频部分; 然后对低频系数采用自适应尺寸分块法进行融合, 图像块的尺寸由改进的果蝇算法优化求解, 精细化处理低频融合结果, 得到一幅能精确到每个系数来源的标签图; 再利用高频分量的邻域系数差结合该标签图对高频系数进行融合; 最后重构得到融合图像.实验结果表明, 该算法能够提高融合速度, 解决了空域分块融合容易产生块效应的问题.

In order to improve the fusion efficiency and solve the problem of wrong selection of fusion coefficients in the fusion method based on multi-scale transform, a method based on non-subsampled dual-tree complex contourlet transform combined with adaptive block is proposed for infrared and visible image fusion. Firstly, the source image is decomposed into low-frequency and high-frequency parts by non-subsampled dual-tree complex contourlet transform. For the low frequency coefficients, an adaptive block-based fusion technique is applied, where the optimal block size can be calculated by using the improved drosophila algorithm, and the low-frequency fusion results are refined to obtain a label map which can accurately indicate the origin information of each pixel. Then, the neighborhood coefficient difference of the high-frequency component is used to combine the label map height. Finally, the fused image is reconstructed. The experimental results demonstrate that the proposed method can accelerate the process of fusion and solve the problem of block effect in spatial block fusion.