由于传统的多聚焦图像融合算法不能对图像中聚焦区域划分进行有效度量，提出了一种新的多聚焦图像超分辨融合方法来改善图像融合效果。该方法对图像清晰区和模糊区进行度量，并利用稀疏表示方法对度量后的清晰区域进行超分辨重建。首先，采用空间频率方法提取源图像中清晰区域与模糊区域，然后确定清晰区域中的主清晰区和次清晰区，并计算它们的真实下采样尺度。最后，通过学习多尺度稀疏表示字典对图像中次清晰区域进行超分辨率重建，并与清晰区域结合形成最终融合图像。实验及各种定量评价结果表明，提出的方法较常规方法具有更好的融合性能，得到的图像更清晰。对比Harr小波，非下采样轮廓波变换(NSCT)，剪切波(Shearlet)变换等方法，其熵(EN)提升了1%，峰值信噪比(PSNR)提升了0.62 dB，清晰度(SP)和空间频率(SF)提升30%，均方误差(MSE)下降了6%左右。

As traditional multi-focus image fusion methods can not effectively measure the partitioning focus regions in images, a novel algorithm by using super-resolution image reconstruction for multi-focus image fusion was proposed to solve the problem. The algorithm measured the in-focus and out-of-focus regions and performed the super-resolution image reconstruction for the clear area with sparse representation. Firstly, the spatial frequency method was used to extract the in-focus and out-of-focus regions in source images. Then, the main-clear and sub-clear parts within in-focus regions were identified and their real down-sampling scales for each part were calculated. Finally, the sub-clear parts were reconstructed in super-resolution through learning multi-scale sparse dictionaries and the fused image was obtained by combining the different parts of source images. The experimental results show that the proposed method can provide clear images and better facus performance. As compared with the conventional methods, such as Harr wavelet, Nonsubsampled Contourlet Transform (NSCT), and shearlet transform,the proposed method enhances its Entropy (EN) and Peak Signal-to-Noise Ratio (PSNR) by 1%,and 0.62dB, respectively, the clarity (SP) and spatial frequency (SF) by 30%, and the Mean Square Error (MSE) is decreased by about 6%.