# Dual Enhancement Network for Infrared Small Target Detection
## 2 Related Work  
For infrared small target detection, both single-frame image-based detect before track (DBT) and sequential-frame image-based track before detect (TBD) [8] techniques are very important methods. The DTB technique can analyse single-frame infrared images and use target detection algorithms to achieve the localisation and identification of small targets, which is suitable for static or slow-moving scenarios, thus disregarding the information of time [9]. The TBD technique, on the other hand, is more suitable for dealing with motion changes in infrared small targets, and by analysing a continuous sequence of infrared images, the motion trajectory and appearance changes of small targets can be better captured. In practical applications, for scenarios that require high real-time requirements and limited resources, single-frame image-based detection can complete the target detection and tracking tasks more quickly and, at the same time, the progress of single-frame image detection algorithms will also directly affect the performance of DBT techniques. Methods for traditional IR small target detection are mainly classified as the following: filter-based [10; 11], local information-based [12; 13], data structure-based [14; 15], and deep learning-based methods [8; 16; 17], but there are some limitations in coping with complex backgrounds and small target detection. Firstly, unlike common small object detection, infrared images contain many complex backgrounds, and infrared small targets are very small and sparse, sometimes even containing only one pixel. Therefore, it is essential to reduce the interference of background noise and pay attention to small targets simultaneously in the detection process. With the development of deep learning technology, deep learning models are gradually replacing the traditional methods, and they can deal with the infrared small target detection problem in complex scenes more effectively. For most semantic segmentation, IRSTD has its unique characteristics, such as filter-based methods [18], low-rank-based methods [14; 19], and data-driven methods [7; 20; 21; 22; 23; 24], but few of them consider the challenges of complex backgrounds and fuzzy targets in infrared images. [PERSON] et al. [7] proposed a deep adversarial learning framework called MDvsFA-cGAN that includes two generators and a discriminator to balance false detections and false alarms. [PERSON] et al. [20] designed an asymmetric context modulation (ACM) module to better highlight small objects. Shortly thereafter, [PERSON] et al. [21] proposed the attentional local contrast network (ALCNet) with a feature graph cyclic shifting scheme and bottom-up attentional modulation. [PERSON] et al. [22] proposed ISNet to detect precise shape information and effectively suppress complex background noise by aggregating different layers of edge information. [PERSON] et al. [23] advocated for the UIU-Net to learn multi-level and multi-scale representations. [PERSON] et al. [24] offered a dense nested attention network (DNA-Net) to fully merge and utilise contextual information from small targets. [PERSON] et al. proposed the RISTDnet network [25], which combines a hand-designed feature approach convolutional neural network to create a multi-sized feature extraction framework, introducing a feature mapping network, and the thresholding operation and segmentation techniques are able to extract the target accurately from the complex background. [PERSON] et al. proposed that a patch-based approach is more effective than other filter-based approaches [26]. This paper presents a new network, DENet, to address the IRSTD task. We believe that the intricacies of infrared camera equipment and environmental conditions significantly compound the difficulty of detecting small targets in infrared imagery, transcending the realm of conventional image-processing techniques alone. As a result, we should identify the global information and, specifically, improve the local information in accordance with the relevant occasions.