中文摘要：

针对医学图像复杂多样的特点,提出一种基于在线字典学习的自适应医学图像融合算法。该算法首先利用在线字典学习理论训练源图像的过完备字典;然后利用正交匹配追踪算法对源图像进行稀疏分解得到稀疏编码,根据源图像之间稀疏编码的能量差异程度和梯度差异程度自适应调整融合准则,若能量差异程度大于梯度差异程度,则根据能量取大准则融合稀疏编码,反之,根据梯度取大准则融合稀疏编码;最后将融合后的稀疏编码与过完备字典进行重构得到融合图像。实验结果表明：与多尺度几何分析、K奇异值分解等图像融合算法比较,该算法融合的图像客观评价指标信息熵、边缘评价因子均有所提高,主观上纹理清晰、对比度高,能够很好地保留源图像的边缘信息。

英文摘要：

According to characteristics of complexity and diversity of medical image, the paper proposed an adaptive medical image fusion algorithm based on online dictionary learning. The algorithm first applies the theory of online dictionary learning to train over complete dictionary of source images, and then adopts orthogonal matching pursuit algorithm for sparse decomposition of source images to gain sparse codes. Besides,it adjusts the fusion rules adaptively according to the degree of energy difference and gradient difference of sparse codes between source images. If the degree of energy difference is more than that of gradient difference,the sparse codes are fused on the basis of the rules of maximum energy. On the contrary,the sparse codes are fused according to the rule of maximum gradient. Finally, the fused sparse codes and over complete dictionary are reconstructed to obtain fused images. The experiment result shows that compared with multiscale geometric analysis, k-singular value decomposition and other image fusion algorithms,objective evaluation indexes of images fused by this algorithm-information entropy and edge evaluation factor improve. Subjectively, the texture of the fused image is clearer, and the contrast ratio is higher. In addition, this algorithm can well preserve edge information of source images effectively.