# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 2 Related Work  
### Infrared Small Target Detection Based on a Single Frame  
Frame-based infrared small target detection refers to the process of detecting small targets in individual frames of raw infrared images. Typically, this is achieved by employing image processing techniques to enhance the targets or differentiate them from the background, followed by threshold segmentation for target recognition, without considering temporal sequences. Such methods are characterized by their concise approach, low computational complexity, fast detection speed, and ease of deployment on hardware platforms.  
In 2013, [PERSON] et al. [15] proposed the Infrared Patch Image (IPI) model for infrared small target detection. The approach involves dividing the image into blocks using a sliding window, followed by vectorizing the block matrices and rearranging them into a data matrix. Robust principal component analysis (RPCA) is employed to decompose the data matrix into a low-rank matrix and a sparse matrix. Finally, the sparse matrix is reconstructed into an image, yielding the target image. In 2014, [PERSON] et al. [16] proposed a fast target acquisition method for infrared small target detection based on the human visual system (HVS) attention transfer mechanism, which incorporates thresholding operations and a fast traversal mechanism. In 2016, [PERSON] et al. [17] proposed a multi-scale image patch contrast measurement method, inspired by biology, aiming to enhance the contrast between the target and the background, to achieve the detection of small targets through a simple adaptive threshold segmentation method. In 2016, [PERSON] et al. [18] proposed an effective small target detection method based on weighted image entropy. This method improves the signal-to-noise ratio between interfering objects and small targets in a scene that have similar thermal intensity measurements to the background through multi-scale grayscale difference weighted local entropy measurements, combined with adaptive threshold operations. In 2016, [PERSON] et al. [19] proposed the weighted infrared patch-image (WIPI) model, which introduced structural prior information into the separation process of weak infrared targets and background, and added an adaptive coefficient to each image block, to replace the one used in the original IPI model, which had a unified coefficient.  
Traditional methods cannot cope with changing scenarios with complex backgrounds. These methods usually need to adjust the parameters of each scenario according to each scenario, which brings challenges with application in actual scenarios. In recent years, methods based on deep learning have also been applied in the field of infrared small target detection. In 2015, [PERSON] et al. [20] first used a phase spectrum Fourier transform to extract salient areas that may contain targets, and then used SVM to classify these areas to confirm the areas of small targets. In 2017, [PERSON] et al. [21] used a multi-layer fully connected neural network to detect small infrared targets. The neural network only classified image blocks of size 21 \(\times\) 21 and identified targets and non-targets. Its limited training data limited the detection ability of the model. In 2017, [PERSON] et al. [22] used a context aggregation network as the basic network. The networks were connected to each other using U-net form, and they proposed a loss function based on missed detection and false detection to solve the problem of the serious imbalance between target and background duty cycles. The internal attention aware network (IAANet) explores the relationship between image pixelsand enhances the correlation of different targets through attention at different scales [23]. DNANet proposed the DNIM module, which improves target feature extraction and preservation effects through dense connections and spatial pyramid fusion [24].