目的 由于一些光学镜头聚焦范围的有限性，很难对同一场景中所有物体都清晰地成像在一幅图像中，而将同一场景中的多幅源图像进行融合可以得到一幅全景更加清晰的图像,为了增强融合图像的质量，提出了一种新的非下采样四元数剪切波变换（NSQST）的图像融合算法。方法 首先将源图像经过NSQST分解得到低频子带系数和高频子带系数；其次，对低频子带，提出了一种改进的稀疏表示（ISR）的融合规则；对于高频子带，提出一种改进的空间频率、边缘能量和局部区域相似匹配度相结合的融合规则；最后通过NSQST逆变换得到融合图像。结果 与其他5种融合方法进行对比，本文方法获得了较好的客观指标和视觉效果，其中与NSCT-SR算法相比，本文方法获得的4个客观指标分别提高了3.6%、2.9%、1.5%、5.2%，3.7%、3.2%、3.2%、3.0%和6.2%、3.8%、3.4%、8.6%。结论 通过多聚焦图像进行融合实验，实验结果表明该方法可进一步应用于目标识别、医学诊断等领域。

Objective Obtaining an image that contains all objects in focus is difficult because of limited depth of focus of optical lenses. Image fusion target aims to generate a sharper image by integrating complementary information from multiple source images of the same scene. To improve fused-image quality, a novel algorithm based on non-subsampled quaternion shearlet transform (NSQST) is proposed in this paper. Method First, source images are decomposed by NSQST to obtain low- and high-frequency sub-band coefficients. For low-frequency sub-band coefficients, improved sparse representation-based fusion rule is presented; then, for high-frequency sub-band coefficients, a scheme that combines new, improved spatial frequency, edge energy, and local similarity-matched degree is presented. Finally, a fused image is obtained by performing inverse NSQST. Result The proposed method can obtain better visual effects and objective evaluation criteria compared with other five fusion methods. Fusion quality indexes have increased by 3.6%, 2.9%, 1.5%, 5.2%, 3.7%, 3.2%, 3.2%, 3.0%, 6.2%, 3.8%, 3.4%, and 8.6% compared with the result of the NSCT-SR method. Conclusion A multi-focus image is used in our experiment, and experimental results show that this method can be further applied in target recognition, medical diagnosis, and other fields.