Infrared Small Target Detection Method Based on Low Rank Model with Local Contrast Prior

In order to solve the problem that infrared small target detection algorithm is easy to detect falsely at the edge and inflection point of complex background,an infrared small target detection algorithm based on the fusion of local contrast and non-local low-rank tensor model is proposed in this paper. First, Double window local contrast measure algorithm is used to extract the local prior information of target and background. Then,under the constraints of local prior information obtained,the standard IPT model was reconstructed,and weighted tensor nuclear norm minimization was introduced to suppress the background and improve the iteration efficiency. Finally, the separation problem of target and background is transformed into a tensor robust principle component analysis problem,and alternating direction method of multipliers is used to solve this problem. Experimental results show that the performance of the proposed method is better than the existing typical infrared small target detection methods under different complex backgrounds.