## 3 Experiments and Analysis
### Comparison of Fusion Criteria
#### 3.2.2 High Frequency Fusion  
The high frequency sub-band image contains the detailed feature information from the source image, such as target contour and terrain texture. On the basis of the optimal low frequency fusion criterion of SMLF energy, experiments are conducted with Data 1 and 2, and the performance of high frequency fusion criteria is evaluated by the fused image and difference image (Figures 8 and 9). The fusion criteria include the following: (1) Energy_Max; (2) Direction contrast maximum (Dire_Contrast_Max); (3) PCNN (NSML); (4) IDPCNN (MSF) [25]; (5) IDPCNN (NSML). Among the PCNN and IDPCNN model, ignition time and link strength are represented by the soft limiting function and AG value, respectively, which are the optimal choice directly. It mainly discusses the influence of external excitation value on these models, which are represented by MSF and NSML, respectively. Figure 8: Fused image (left) and difference image (right) of Data 1. (**a**) Energy_Max. (**b**) Dire_Contrast_Max. (**c**) PCNN (NSML). (**d**) Improved DPCNN (IDPCNN) (modified spatial frequency (MSF)). (**e**) IDPCNN (NSML). Figure 8b shows that the contrast of fused image is reduced and the feature of detail changes is smoothed, while other fused images are basically consistent with the source image, with good subjective visual effects. In addition, the difference image of Figure 8b displays that the remaining information leaves a lot on the left side, and the information extraction of the aircraft contour edge is not sufficient, which indirectly indicates that the Dire_Contrast_Max criterion is not applicable to sonar image fusion. The difference image of Figure 8a shows that there still exists an insufficient partial integration. The remaining information of Figure 8c-e is almost 0, which corresponds to the clear left area and blurred right area of source image. Better visual effects illustrate that the high frequency fusion model of the PCNN family (criterion 3, 4, 5) can fully extract the source image favorable information, which is more suitable for sonar image fusion. Figure 9b shows that the information extraction of texture and features is not sufficient, but other fused images have good visual effects. In addition, the difference images display that the residual error in Figure 9b is the greatest, followed by the fusion criterion of Energy_Max, which fully reflects the poor ability of the two criteria in processing strip fusion. On the contrary, the residual errors on the left side of Figure 9c, d are almost 0. The terrain and geomorphology texture of the fused image are clear, and the peripheral contour of the petroleum pipeline has no false contour phenomenon, which basically inherits the detail information features from the source image. Figure 9: Fused image (top) and difference image (bottom) of Data 2. (a) Energy_Max. (b) Dire_Contrast_Max. (c) IPCNN (MSF). (d) IDPCNN (NSML). From the perspective of subjective vision, the fused images of criteria 3, 4, and 5 meet the requirements of high information retention. In order to further evaluate the performance of the criterion, some objective indexes are analyzed quantitatively, as shown in Tables 3 and 4. Tables 3 and 4 show that each index value based on the criterion of Dire_Contrast_Max is the smallest. For example, the index values of AG, FD, and \(Q^{AB\!F}\) are only 3.9657, 5.3125, and 0.4134 in Data 1, which indicates that the fused image deviates greatly from the source image and exhibits the phenomenon of distortion. The index value of maximum energy is not as good as that of the PCNN family. The quality of the fused image can be improved by using the PCNN family model to select the coefficients. Moreover, compared with the traditional PCNN model, the index values of the IDPCNN series are relatively larger. This is because the IDPCNN model combines the input of information from both source images, which reduces the ability of misjudging the information source.