Image fusion experiments were carried out on different images. Figure 6 depicts a pair of medical images; the left image is computed tomography (CT) image, and the right one is magnetic resonance imaging (MRI) image. The CT image shows structures of bone, while the MRI image shows the areas of soft tissue details. Figure 7 shows the fused images by various tested methods, and the local amplification of these results is shown in Fig. 8 for easy observation. Figures 7(a) and 8(a) reveal that the DWT-based method produces more artificial images. From the right image in each set of Fig. 8, we can see that, motivated by the multiscale transform, the SWT-, NSCT-, and LPSSIM-based methods reserve the details more completely than SR-, SOMP-, and JSR-based methods. However, from the left side, it can be seen that SR-, SOMP-, and JSR-based methods have much clearer skeletal features than SWT, NSCT, and LPSSIM fused images, due to the sparse representation, which can extract the salient features of source images. What is more, the NSCTSR fused image exhibits better visual quality with much clearer soft tissues and bone structures than compared methods. Second is the method of optimal directions for joint sparse representation-based image fusion (MODJSR) fused image, which loses only some soft tissue details as can be seen in the left image in Fig. 8(h), while the details are also important for diagnosing. Table 1 reports the objective evaluation of various methods and the best results are indicated in bold. We can see that the NSCTSR-based method achieved the best results in four of the five evaluation metrics, i.e., , , , . As for , the MODJSR method performed slightly better than our method.