Infrared target detection performance enhancement using TSR-LMS algorithm

Infrared weak target detection is one of the key technologies in infrared search and tracking. It is widely used in maritime surveillance systems and reconnaissance systems, with significant potential for civilian applications such as security monitoring, forest fire detection, and power equipment inspection. However, detecting infrared weak targets in low-resolution images faces challenges such as small target size, sparse texture information, complex backgrounds, and low signal-to-noise ratio(SNR). Inspired by the positive impact of super-resolution on target detection, we introduced an improved version of the classic Temporally Selective Regularized Least Mean Squares (TSR-LMS) video super-resolution algorithm to enhance target detection performance. To further reduce noise introduced by registration errors, we employ the Demons registration algorithm to address nonlinear transformations between adjacent frames. The effectiveness of our algorithm is validated on publicly available infrared target detection datasets and real image sequences captured by infrared cameras. Experimental results demonstrate that the method effectively suppresses background clutter interference while improving target signal-to-noise ratio. To further validate the effectiveness of the algorithm, we selected several representative infrared small target detection algorithms and performed target detection on image sequences from multiple different scenarios, comparing the detection results before and after reconstruction. The results demonstrate a significant improvement in the average AUC of the reconstructed images across image sequences from various scenarios. Thus, our method significantly enhances the detection probability of traditional single-frame and multi-frame target detection algorithms while reducing their false alarm rate, demonstrating broad application prospects in infrared target detection.