Patch-Based Semantically Enhanced Network for IR Dim and Small Targets Background Suppression

The task of background suppression in infrared small-target scenarios aims to eliminate irregular noisy backgrounds while preserving targets with high-frequency features. In infrared small-target scenes at long distances, the backgrounds become complex and the target features are degraded, highlighting a significant disparity between the detailed and realistic background and the limited features of the targets. To address these challenges, we propose a patch-based semantically enhanced generative adversarial network (GAN) named PSEnet for background suppression in infrared small-target scenarios. First, we introduce a patch-scale GAN that allows the model to concentrate on local background suppression. This shift from a global to local perspective simplifies the complexity of background suppression. Second, we employ the PSE module consisting multiscale dilated convolution and adaptive weight fusion to extract local semantic information. Third, by segmenting the infrared image into smaller patches and resampling them, we create a more balanced dataset for adversarial training. Experimental results demonstrate that the proposed algorithm significantly improves the signal-to-noise ratio of dim and small targets, reduces the missing detection rate, and achieves a precision of almost 91%. In conclusion, this approach effectively uses GANs for background suppression in complex environments.