Infrared Patch-Tensor Model With Weighted Tensor Nuclear Norm for Small Target Detection in a Single Frame

The robust and efficient detection of infrared small target is a key technique for infrared search and track systems. Several robust principal component analysis (RPCA)-based methods have been developed recently, which have achieved the state-of-the-art performance. However, there are still two drawbacks: 1) the false alarm ratio would raise under the heavy background clutters and noises and 2) these methods are usually time-consuming and not suitable for real-time processing. To solve this problem, an infrared patch-tensor (IPT) model based on weighted tensor nuclear norm is proposed in this paper. First, the infrared image is transformed into the IPT. Considering the sum of nuclear norms adopted in the IPT model is not the convex envelope of the tensor rank, and the solution is substantially suboptimal. The tensor nuclear norm is adopted to recover the underlying low-rank background tensor and sparse target tensor, and the computation complexity can be reduced dramatically with the help of the tensor Singular Value Decomposition. Moreover, to further suppress the background clutters, a weight tensor is incorporated with tensor nuclear norm to preserve the background edges better. Then, the separation between target and background is formulated as a convex weighted tensor RPCA model. Finally, the proposed model can be solved by the alternating direction method of multipliers. Extensive experiments demonstrate that the proposed model outperforms the other state-of-the-arts in terms of performance and efficiency.