Infrared Small Target Detection Method Based on Frequency Domain Clutter Suppression and Spatial Feature Extraction

With the continuous development of infrared technology, the application of infrared imagery is increasingly widespread. Nonetheless, infrared imagery suffers from low contrast and high noise characteristics, making it challenging to detect and recognize infrared small targets. Frequently, existing deep learning-based infrared target detection methods solely employ spatial information, neglecting frequency domain information, thereby creating susceptibility to background clutter interference and low detection accuracy in complex scenes. Here, a deep learning-based method is proposed that combines frequency domain clutter suppression and spatial feature extraction to detect infrared small targets. Firstly, the spatial domain infrared image is transformed into the frequency domain using block discrete cosine transform (DCT). Secondly, the frequency domain information is filtered using an attention mechanism to adaptively enhance the targets and suppress the background clutter. Finally, the compressed attention map is transformed back to the spatial domain and inputted into a lightweight neural network for spatial feature extraction and fusion to obtain detection results. This approach enhances detection accuracy, and reduces computational complexity and storage space, thereby making it practical and deployable. Experimental outcomes demonstrate strong detection capabilities on public datasets, and significant advantages over traditional and existing deep learning methods.