一种基于稀疏表示的红外与微光图像的融合方法

根据人类视觉系统及信号的过完备稀疏表示理论,提出了一种基于稀疏表示的红外与微光图像融合算法。该方法首先把图像分割成部分重叠的图像块,由正交匹配追踪算法完成图像块的稀疏分解;然后采用最大值融合准则选择融合系数并完成图像块的重构,得到融合结果图像。实验结果表明,本文算法的融合效果优于小波变换法、Laplacian塔型方法以及PCA方法等传统融合方法。

Infrared and Low-level-light Image Fusion Based on Sparse Representation

An infrared and low-level-light image fusion algorithm based on image sparse representation is proposed according to human visual systems and the over-complete sparse representation. In the method, an image is segmented into partly overlapped image patches firstly. The image patches are decomposed by an orthogonal matching pursuit algorithm. Then, the maximum fusion rule is used to choose suitable fusion coefficients for the reconstruction of image patches. Thus, the fused images are obtained. The experimental result shows that compared with the traditional fusion methods such as wavelet transform, Laplacian pyramid and principal component analysis methods, the proposed method has better fusion effectiveness.