Abstract：In the fields of infrared detection and infrared tracking, infrared small target detection is widely applied. However, infrared small target detection poses significant challenges. The existing methods for infrared small target detection fail to address complex background issues while losing detailed information during feature extraction. Therefore, an infrared small target detection network inspired by high-order differential equations is proposed. Under the guidance of the interpretable theory, a fourth-order Adams-guided feature fusion module is designed, incorporating adaptive weight factors to effectively fuse multi-scale information from different levels. High-order difference equations are employed to eliminate redundant information through deep learning. The target feature enhancement module utilizes a residual structure composed of convolutions at different scales to suppress background noise and enhance multi-scale features with high information content. Experiments for small target detection on publicly available SIRST dataset show that the proposed network has advantages in the evaluation metrics and visual quality.
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2023, Vol. 36 
