Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

Abstract

Infrared small target detection plays an important role in many infrared systems. Recently, many infrared small target detection methods have been proposed, in which the lowrank model has been used as a powerful tool. However, most low-rank-based methods assign the same weights for different singular values, which will lead to inaccurate background estimation. Considering that different singular values have different importance and should be treated discriminatively, in this paper, we propose a non-convex tensor low-rank approximation (NTLA) method for infrared small target detection. In our method, NTLA adaptively assigns different weights to different singular values for accurate background estimation. Based on the proposed NTLA, we use the asymmetric spatial-temporal total variation (ASTTV) to thoroughly describe background feature, which can achieve good background estimation and detection in complex scenes. Compared with the traditional total variation approach, ASTTV exploits different smoothness strength for spatial and temporal regularization. We develop an efficient algorithm to find the optimal solution of the proposed model. Compared with some state-of-the-art methods, the proposed method achieve an improvement in different evaluation metrics. Extensive experiments on both synthetic and real data demonstrate the proposed method provide a more robust detection in complex situations with low false rates.

Infrared Dim and Small Target Detection from Complex Scenes via Multi-Frame Spatial–Temporal Patch-Tensor Model

“…proposed an edge and corner awareness-based spatial-temporal tensor model (ECA-STT) by introducing an edge-corner awareness indicator and adding a tensor-based non-convex tensor low-rank approximation (NTLA) regularization term to the model. Liu [36] preserved more information in the spatial-temporal domain by giving different weights to the spatial TV norm and the temporal TV norm, and thus proposed a new model, which shows a better performance in complex scenes. Kong [37] used a Log operator to replace the L 0 norm to approximate the rank of the background, also adding the spatial-temporal TV norm and thus proposed infrared small-target detection via non-convex tensor-fibered nuclear norm rank approximation (LogTFNN).…”

“…To alleviate the problem of insufficient spatial information of target objects, Qi et al [40] proposed a single-stage small-object detection network (SODNet) to detect small objects after integrating professional feature extraction and information fusion technology. Due to the lack of infrared datasets [41] and the characteristics of images [36], the development of deep learning in infrared target detection is relatively difficult.…”

“…Solving the traditional L 0 norm is an NP-hard problem, and using L 1 norm instead is a popular method because the L 1 norm is the best convex approximation to the L 0 norm, and it is easier to solve. Therefore, for the sparsity of the target, referring to the methods of other models [21,25,36,53], we use the L 1 norm to measure. Therefore, our model is updated to: min…”

“…The detection probability and false alarm rate are used to measure the performance of the algorithm [36]. Detection probability is defined as:…”