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. 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. 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. 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.