# Dual Enhancement Network for Infrared Small Target Detection
## 5 Conclusions  
In this paper, a dual enhancement network (DENet) is proposed to solve the problem of complex backgrounds and dim targets in infrared images. The DENet consists of two key modules: the residual sparse enhancement (RSE) module and the spatial attention enhancement (SAE) module. The RSE module effectively mitigates the impact of complex backgrounds by filtering out redundant information, thereby highlighting the salience \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Ablation Module**} & \multicolumn{3}{c}{**MUAA-SIRST**} & \multicolumn{3}{c}{**[20]**} & \multicolumn{3}{c}{**NUDT-SIRST**} & \multicolumn{3}{c}{**[24]**} & \multicolumn{3}{c}{**IRSTD-1K**} & \multicolumn{3}{c}{**[22]**} \\ \cline{2-13}  & **R** & **S** & **SSP** & **IoU** \(\uparrow\) & **P\({}_{d}\)** \(\uparrow\) & **F\({}_{a}\)** \(\downarrow\) & **IoU** \(\uparrow\) & **P\({}_{d}\)** \(\uparrow\) & **F\({}_{a}\)** \(\downarrow\) & **IoU** \(\uparrow\) & **P\({}_{d}\)** \(\uparrow\) & **F\({}_{a}\)** \(\downarrow\) \\ \hline baseline & \(\times\) & \(\times\) & \(\times\) & 74.33 & 95.06 & 19.67 & 85.48 & 98.62 & 7.882 & 61.13 & 90.59 & 18.37 \\ w/o \(R\) & \(\times\) & ✓ & \(\times\) & 75.63 & 96.90 & 17.77 & 86.91 & 98.91 & 4.667 & 62.58 & 91.93 & 17.30 \\ w/o \(SSP\) & ✓ & ✓ & \(\times\) & 75.72 & 96.95 & 15.67 & 88.24 & 99.2 & 3.543 & 63.42 & 92.31 & 15.32 \\ w/o \(S\) & ✓ & \(\times\) & ✓ & 75.95 & 96.96 & 15.13 & 88.60 & 99.05 & 3.125 & 63.81 & 92.94 & 11.41 \\ DENet (Ours) & ✓ & ✓ & ✓ & 77.55 & 98.10 & 13.50 & 89.66 & 99.26 & 2.987 & 64.78 & 93.94 & 10.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation study of each component on the NUAA-SIRST, NUDT-SIRST, and IRSTD-1K datasets. R, S, and SSP represent the RSE module, the SAE module, and the SSP module, respectively. The best results are in red. _IoU_, _P\({}_{d}\)_ and _F\({}_{a}\)_ are expressed in percentages (%). The up arrow next to the assessment indicator indicates that a larger value is better, and the down arrow indicates that a smaller value is better. A cross (\(\times\)) indicates that the corresponding module is not used, a tick (✓) indicates that the corresponding module is used. Figure 8: Ablation visual comparisons on the NUAA-SIRST dataset. For better visualization, the correct detected targets are enlarged in the right-bottom corner. The correctly detected targets and false alarm areas are highlighted by red rectangles and blue circles, respectively. of the target region. Consequently, DENet successfully suppresses interference from complex backgrounds and improves target detection accuracy. Meanwhile, the SAE module automatically enhances the features of dim and small targets in infrared images to make them easier to detect and recognise. Using our DENet, the redundant information of complex backgrounds can be effectively filtered, and the contextual information of dim small targets can be well integrated and fully utilised to enhance the features. Our proposed DENet method demonstrated outstanding performance on three publicly available infrared datasets. It not only surpassed the state-of-the-art IRSTD method in terms of segmentation speed but also achieved superior detection results in visual analysis. In our future research, we will prioritize the exploration of novel approaches that integrate deep learning and image-processing techniques to further enhance the accuracy and efficiency of target detection in infrared images. Conceptualisation, X.W. and X.H. ; methodology, H.L. and C.L. ; software, L.Z. and W.H. ; validation, C.L., X.W. and W.H. ; formal analysis, X.W. ; investigation, H.L. ; resources, L.Z. ; data curation, W.H. ; writing--original draft preparation, X.W. and C.L. ; writing--review and editing, X.W. and X.H. ; visualisation, H.L. ; supervision, X.H. ; project administration, X.W. ; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.