# Dual Enhancement Network for Infrared Small Target Detection
## 4 Experiment
### Ablation Study  
(1) _Effectiveness of each component_: We conducted ablation studies to assess the effectiveness of each component in our proposed DENet. The quantitative results are presented in Table 2, and the qualitative results are showcased in Figure 8. R, S, and SSP represent the RSE module, the SAE module, and the SSP module, respectively. In our method, SSP is inserted into R. We can observe that directly using baseline cannot obtain satisfactory results because it does not take the special characteristics (complex backgrounds, exceptionally dim and small targets) of the infrared images into account. The experiment results clearly demonstrate the effectiveness of each module in our proposed approach. As expected, the best results were obtained by simultaneously suppressing complex backgrounds and enhancing dim small targets in the infrared images.  
Additionally, to further validate the effectiveness of SAE, we also conducted a comparison between \"baseline + SAE\" and \"baseline + CA (Channel Attention) module [32]\" on NUAA. The results reveal that the former achieved a better IoU/Pd/Fa (75.63/96.95/17.77), whereas the latter yielded a worse IoU/Pd/Fa (74.84/96.83/18.72), indicating the superiority and pertinence of the SAE module in the IRSTD task. Compared with the regular attention mechanism (CA), SAE places greater emphasis on dim small targets within intricate backgrounds and is capable of suppressing the interference of background clutter while highlighting the dim small targets.  
Figure 5: Visual comparisons of different IRSTD methods on the IRSTD-1K dataset. The correctly detected targets, miss detection areas, and false alarm areas are highlighted by red rectangles, blue rectangles, and blue circles, respectively.  
Figure 6: Qualitative results achieved by different IRSTD methods. The correctly detected targets, miss detection areas, and false alarm areas are highlighted by red rectangles, blue rectangles, and blue circles, respectively.  
Figure 7: ROC curve comparisons on the NUAA-SIRST, NUDT-SIRST, and IRSTD-1K datasets.  
(2) _Impact of RSE_: Comparing the other modules, we found that RSE with SSP inserted has the largest contribution to the whole model, which can effectively suppress the background noise, and at the same time has a good segmentation effect and performs optimally in the NUDT-SIRST dataset. If only the SAE module is used to adaptively enhance the features of dim small targets, the targets are easily submerged in the noise. Therefore, the combination of the two is optimal.  
(3) _Impact of SAE_: Although SAE can reduce noise and improve model performance to some extent, there are still some limitations to its optimisation capabilities. This is because its enhancement of a wide range of dim small targets requires careful parameter tuning for optimal results in complex visual tasks, and difficulties in parameter tuning may result in less than optimal model performance.  
(4) _Impact of lambda_: The sensitive study of balance parameter \(\lambda\) is presented in Table 3. We investigated the effect of varying the value of \(\lambda\) from 0.1 to 0.9 on the NUAA-SIRST dataset, and set the default lambda as 0.8 with the best performance.