Infrared small target detection method based on improved non-convex estimation and asymmetric spatial-temporal regularization
Aiming at infrared dim and small targets detection in complex background,a new kernel norm estimation method was proposed based on the non-convex tensor low-rank approximation algorithm with asymmetric spatial-temporal total variation regularization,replacing the original estimation method in the algorithm.An adaptive weight tensor based on structure tensor and multi-structure element Top-Hat filtering was proposed to constrain the target tensor,which had enhanced the sparsity and suppressed the remaining strong edge structures of the target tensor.Experimental results show that the proposed improved algorithm can better eliminate the influence of strong edge structure on the detection results,and has a lower false alarm rate than the original algorithm under the condition of ensuring the detection rate.
infrared small target detectiontensor recoverytensor nuclear normmulti-structure element Top-Hat filtering
万方数据
维普
