针对现有的红外与可见光图像融合算法存在融合图像的对比度与清晰度降低和细节纹理信息丢失等问题,提出将鲁棒主成分分析(RPCA)、压缩感知(CS)和非下采样轮廓波变换(NSCT)相结合的融合算法。首先对两幅源图像分别进行预增强处理,应用RPCA分解得到相应的稀疏分量和低秩分量;然后对稀疏矩阵利用结构随机矩阵压缩采样,利用高斯梯度-信息反差对比度(GG-DCI)压缩融合,经正交匹配追踪法(OMP)重构;接着对低秩矩阵采用NSCT分解成低频子带和高频子带,低频子带选用区域能量-直觉模糊集(RE-IFS)融合,最高频子带利用最大绝对值规则融合,其他高频子带选用自适应高斯区域方差融合;最后将融合后的稀疏分量和低秩分量叠加得到融合图像。实验结果表明,本文算法相比其他算法能够更好地提高融合图像的对比度和清晰度,保留了丰富的细节纹理信息,客观评价指标也总体优于现有算法,有效提升了红外与可见光图像的融合效果。

Existing fusion algorithms for infrared and visible images face issues such as low contrast and clarity of fused image and loss of detailed texture information. To address these problems, a fusion algorithm combining robust principal component analysis (RPCA), compressed sensing (CS), and non-subsampled contour transform (NSCT) is proposed. Firstly, two original images are pre-enhanced, and the pre-enhanced images are decomposed via RPCA to obtain the corresponding sparse and low-rank components. Secondly, the sparse matrices are compressed and sampled using the structural random matrix. Gauss gradient-differential contrast of information (GG-DCI) is used to compress and fuse the images, and the reconstruction is conducted using the orthogonal matching tracking method (OMP). Then the low-rank matrices are decomposed into low- and high-frequency components via NSCT. The low-frequency components are fused using the regional energy-intuitionistic fuzzy set (RE-IFS), the highest-frequency components are fused using the maximum absolute value rule, and other high-frequency components are fused using the adaptive Gaussian region variance. Finally, the fused images are obtained by superimposing the fused sparse and low-rank components. Experimental results show that compared with other algorithms, the proposed algorithm can more effectively improve the contrast and clarity of fused images, retain abundant detailed texture information, possess generally better objective evaluation indexes, and efficiently improve the fusion effect of infrared and visible images.