# Dual Enhancement Network for Infrared Small Target Detection
###### Abstract  
Infrared small target detection (IRSTD) is crucial for applications in security surveillance, unmanned aerial vehicle identification, military reconnaissance, and other fields. However, small targets often suffer from resolution limitations, background complexity, etc., in infrared images, which poses a great challenge to IRSTD, especially due to the noise interference and the presence of tiny, low-luminance targets. In this paper, we propose a novel dual enhancement network (DENet) to suppress background noise and enhance dim small targets. Specifically, to address the problem of complex backgrounds in infrared images, we have designed the residual sparse enhancement (RSE) module, which sparsely propagates a number of representative pixels between any adjacent feature pyramid layers instead of a simple summation. To handle the problem of infrared targets being extremely dim and small, we have developed a spatial attention enhancement (SAE) module to adaptively enhance and highlight the features of dim small targets. In addition, we evaluated the effectiveness of the modules in the DENet model through ablation experiments. Extensive experiments on three public infrared datasets demonstrated that our approach can greatly enhance dim small targets, where the average values of intersection over union (_IoU_), probability of detection (\(P_{d}\)), and false alarm rate (\(F_{a}\)) reached up to 77.33%, 97.30%, and 9.299%, demonstrating a performance superior to the state-of-the-art IRSTD method. infrared image; small target detection; sparse semantic propagation; spatial attention; feature enhancement +
Footnote †: journal: Medicala low signal-to-noise ratio, and \"small\", which means that the target has fewer pixels and sometimes even contains only one pixel. Obtaining particular information about the target during detection is challenging. Therefore, it is necessary to reduce the interference of background noise while focusing on small targets during the detection process. In addition, due to the significant attenuation of infrared radiation energy with distance [7], infrared targets gradually become very dim. As a result, infrared targets are easily submerged in the background clutter, and their ambiguity makes segmentation difficult. In this paper, we propose a dual enhancement network for background noise reduction and dim small target feature enhancement. Specifically, to minimise the interference of complex backgrounds, we propose the residual sparse enhancement (RSE) module, which shifts and spreads a number of representative pixels between any adjacent feature pyramid layers instead of simply summarising them. We achieve the suppression of ambiguous background noise and the enhancement of small target features by propagating sparse key pixels only on infrared small target features (as shown in Figure 1c), generating high-quality features that facilitate subsequent prediction. Thus, the problem of complex backgrounds and unusually small targets can be solved in this innovative way. Furthermore, considering that infrared small targets usually appear very faint and tiny, we propose a spatial attention enhancement (SAE) module to adaptively enhance dim small target features. Detection accuracy can be greatly improved by these enhanced features. Note that this task-specific module enhances the visibility of dim small targets while minimising background noise, thus creating a more distinct and focused visual presentation. In summary, the contributions of this work are as follows:  
* To address the problem of complex backgrounds in infrared images, we propose the residual sparse enhancement (RSE) module, which sparsely selects a number of representative pixels for semantic information propagation, thereby innovatively suppressing background noise.