CAS Key Laboratory of Infrared System Detection and Imaging Technology， Shanghai Institute of Technical Physics of the Chinese Academy of Sciences
Infrared images with complex background are usually characterized by low signal-to-noise ratio (SNR), insignificant variation of adjacent pixels gray scale, and easy interference by clutter signal and noise, which makes detection of small infrared targets difficult. In order to solve the above problems, an infrared dim target detection algorithm based on feature saliency fusion is proposed. Firstly, in the spatial domain, the gray scale difference between the target and its local background is used to calculate the gray saliency map, and in the frequency domain, the significance map after background suppression is obtained by calculating the spectral residuals; Then, the grayscale significance map and frequency domain significance map are normalized and fused with each other by Hadamard product; Finally, the target region is extracted by using adaptive threshold segmentation and Unger filter to eliminate small noise points. Experimental results show that the proposed algorithm can improve the image SNR by tens of times, and has a significant effect on background suppression, and has the advantages of high detection rate and low false alarm rate. It is an effective small target detection algorithm.