提出一种基于超分辨率结合组稀疏表示模型的多聚焦图像融合方法.首先, 使用双三次插值方法增强源图像的分辨率及源多聚焦图像信息; 然后采用自适应稀疏表示学习字典分别对没有明显主导方向和特定主导方向的图像块进行学习, 并采用组稀疏表示模型对源多聚焦图像进行稀疏系数表示; 最后采用最大l1范数来选择最终的表示系数向量.实验结果表明, 所提方法克服了多聚焦图像融合易出现的低空间分辨率和模糊效果的缺点, 具有更好的对比度和清晰度, 主观视觉效果和客观指标均优于传统多聚焦图像融合方法, 在三组图像融合结果的互信息指标上分别领先0.37、0.38和0.32.

A multi-focus image fusion method based on super-resolution combined with group sparse representation model is proposed. First, the bicubic interpolation method is used to enhance the resolution of the source image and the source multi-focus image information. Then, the adaptive sparse representation learning dictionary is used to learn the image blocks without obvious dominant direction and specific dominant direction respectively. The sparse coefficient representation of the source multi-focus image is conducted by the group sparse representation model. Finally, the maximum l1 norm is used to select the final representation coefficient vector. The experimental results show that the proposed method restrains the shortcomings of low spatial resolution and blurring that are easy to appear in multi-focus image fusion, and has better contrast and sharpness. Subjective visual effects and objective indicators show that the proposed method has certain advantages over traditional multi-focus image fusion methods, especially in the mutual information index of the three sets of image fusion results leading 0.37, 0.38 and 0.32 respectively.