Infrared small target detection (IRSTD) is important for many practical applications such as hazardous aircraft warning, especially when the target is not visible in visible light image due to atmospheric conditions such as fog and cloud. However, IRSTD is challenging due to noises, small and dim targets. To address this challenge, we propose a novel Dim2Clear Network (Dim2Clear) for IRSTD in this paper. Specifically, the Dim2Clear consists of a U-Net backbone encoder, a context mixer decoder (CMD) based on spatial and frequency attention (SFA), and an eyeball-shaped enhancement module (EEM). The CMD is composed of cascaded regular residual blocks where two SFA modules are inserted. Each SFA module receives features from different residual blocks and generates spatial attention map from them to modulate the low-level features, which are then decomposed into low and high frequencies using the discrete cosine transformation. Accordingly, features are further modulated according to the generated frequency attention maps. In this way, SFA can extract both spatial context and frequency context to improve the feature representation capacity. In addition, we design an EEM to suppress the noise and enhance the signal-to-noise ratio in the segmentation results from the perspective of image super-resolution. Experiments on the SIRST dataset and our newly constructed IRSTD-1k dataset show that the proposed Dim2Clear outperforms state-of-the-art methods.

Infrared small target detection (IRSTD) refers to extracting small and dim targets from blurred backgrounds, which has a wide range of applications such as traffic management and marine rescue. Due to the low signal-to-noise ratio and low contrast, infrared targets are easily submerged in the background of heavy noise and clutter. How to detect the precise shape information of infrared targets remains challenging. In this paper, we propose a novel infrared shape network (ISNet), where Taylor finite difference (TFD) -inspired edge block and two-orientation attention aggregation (TOAA) block are devised to address this problem. Specifically, TFD-inspired edge block aggregates and enhances the comprehensive edge information from different levels, in order to improve the contrast between target and background and also lay a foundation for extracting shape information with mathematical interpretation. TOAA block calculates the lowlevel information with attention mechanism in both row and column directions and fuses it with the high-level information to capture the shape characteristic of targets and suppress noises. In addition, we construct a new benchmark consisting of 1, 000 realistic images in various target shapes, different target sizes, and rich clutter backgrounds with accurate pixel-level annotations, called IRSTD-1k. Experiments on public datasets and IRSTD-1 k demonstrate the superiority of our approach over representative state-of-the-art IRSTD methods. The dataset and code are available at github.com/RuiZhang97/ISNet.

Infrared small target detection (IRSTD) refers to segmenting the small targets from infrared images, which is of great significance in practical applications. However, due to the small scale of targets as well as noise and clutter in the background, current deep neural network-based methods struggle in extracting features with discriminative semantics while preserving fine details. In this paper, we address this problem by proposing a novel RKformer model with an encoder-decoder structure, where four specifically designed Runge-Kutta transformer (RKT) blocks are stacked sequentially in the encoder. Technically, it has three key designs. First, we adopt a parallel encoder block (PEB) of the transformer and convolution to take their advantages in long-range dependency modeling and locality modeling for extracting semantics and preserving details. Second, we propose a novel random-connection attention (RCA) block, which has a reservoir structure to learn sparse attention via random connections during training. RCA encourages the target to attend to sparse relevant positions instead of all the large-area background pixels, resulting in more informative attention scores. It has fewer parameters and computations than the original self-attention in the transformer while performing better. Third, inspired by neural ordinary differential equations (ODE), we stack two PEBs with several residual connections as the basic encoder block to implement the Runge-Kutta method for solving ODE, which can effectively enhance the feature and suppress noise. Experiments on the public NUAA-SIRST dataset and IRSTD-1k dataset demonstrate the superiority of the RKformer over state-of-the-art methods.