Infrared small target detection via self-regularized weighted sparse model

Small target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is proposed. To improve the latest infrared patch-tensor (IPT) model, a non-convex tensor rank surrogate merging tensor nuclear norm (TNN) and the Laplace function, is utilized for low rank background patch-tensor constraint, which has a useful property of adaptively allocating weight for every singular value and can better approximate l 0 -norm. Considering that the local prior map can be equivalent to the saliency map, we introduce a local contrast energy feature into IPT detection framework to weight target tensor, which can efficiently suppress the background and preserve the target simultaneously. Besides, to remove the structured edges more thoroughly, we suggest an additional structured sparse regularization term using the l 1 , 1 , 2 -norm of third-order tensor. To solve the proposed model, a high-efficiency optimization way based on alternating direction method of multipliers with the fast computing of tensor singular value decomposition is designed. Finally, an adaptive threshold is utilized to extract real targets of the reconstructed target image. A series of experimental results show that the proposed method has robust detection performance and outperforms the other advanced methods.

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Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy

Infrared small-target detection under heterogeneous background, as a challenging task, plays an important role in many applications. In practice, there are not only bright targets but also dim targets, e.g., rescue aircraft and vehicles in the forest fire scene. Considering that most existing infrared small-target detection methods are merely aimed at bright targets, a novel method using multiple morphological profiles (MMP) is proposed, which can detect various types of targets whose brightness varies greatly. In the designed morphological feature extraction, different attributes, i.e., area attribute and height attribute, are applied to extract spatial size and contrast information of small-target in the max-tree and min-tree, respectively. Furthermore, discontinuous pruning values are further utilized for different attributes, and a designed fusion strategy of different pruning values results in more robust detection performance. Experimental results validated on two synthetic data and six real data sets demonstrate that the proposed MMP can not only detect a variety of brightness of targets and different types of targets and kinds of spatial sizes of targets but also further improve the contrast between targets and background, and the background clutter is significantly suppressed.

Infrared Small-Target Detection Based on Multiple Morphological Profiles

Compared with radar and visible light imaging, infrared imaging has its own unique advantages, and in recent years, it has become a topic of intense research interest. Robust small-target detection is one of the key techniques in infrared search and tracking (IRST) applications, and there is no doubt that it has become an investigatory hot spot. In real applications, targets and backgrounds usually change quickly with very high velocities. In addition, a rapidly moving sensor platform typically makes the motion traces of the targets inconsistent. These factors reduce the detection performance of spatiotemporal-based methods, and thus single-frame infrared small-target detection is even more essential. In this survey, existing single-frame infrared small-target detection methods are comprehensively reviewed.

Single-Frame Infrared Small-Target Detection: A survey

Existing infrared (IR) small target detection methods are divided into local priors-based and nonlocal priors-based ones. However, due to heterogeneous structures in IR images, using either local or nonlocal information is always suboptimal, which causes detection performance to be unstable and unrobust. To solve the issue, a comprehensive method, exploiting both local and nonlocal priors, is proposed. The proposed method framework includes dual-window local contrast method (DW-LCM) and multiscale-window IR patch-image (MW-IPI). In the first stage, DW-LCM designs dual-window to compensate for the shortcomings of local priors-based methods, which easily mistake some isolated weak-signal targets. In the second stage, MW-IPI utilizes several small windows with various sizes, which can not only decrease the redundant information generated by sliding windows, but also extract more discriminative information to prevent some pixels in the strong-border edge from being falsely detected. Then, multiplication pooling operation is employed to enhance the target separation and suppress the background clutter simultaneously. Experimental results using five real IR datasets with various scenes reveal the effectiveness and robustness of the proposed method.

Infrared Small Target Detection Using Local and Nonlocal Spatial Information

The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified.

Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

Infrared small target detection plays an important role in precision guidance, infrared warning, and other applications The infrared patch-tensor (IPT) model has good detection performance, but some challenges still exist, such as the inaccurate representation of the background rank and poor robustness against noise and sparse interference In order to solve these problems, a new IPT model is proposed in this article First, to approximate the tensor rank more reasonably, t-SVD is generalized to multimodal t-SVD, and the tensor fibered rank is introduced Moreover, the tensor fibered nuclear norm based on the Log operator (LogTFNN) is used to nonconvex approximate tensor fibered rank Second, to suppress sparse interference such as strong edges and corner points, the prior information is extracted by the local structure tensor Third, the hypertotal variation (HTV) is used as a joint regularization term to remove noise Then, the alternating direction method of multipliers (ADMM) is used to solve the model The proposed algorithm was tested on the 20 single-frame infrared images and six sequences of real scenes Lots of experiments demonstrate that this algorithm has the robustness to noise and different scenes Different evaluation metrics also show that the proposed algorithm has a significant superiority in detection performance compared with various state-of-the-art methods

Infrared Small Target Detection via Nonconvex Tensor Fibered Rank Approximation

In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect.

Cirrus detection based on RPCA and fractal dictionary learning in infrared imagery

Aiming at improving the speed and accuracy of auto-focus for telescope observation, algorithms for image estimation and auto-focus were investigated and are discussed in this article. Based on the image quality assessment, the auto-focusing process of the telescope system is realized by using the mountain-climb search method. Several evaluation functions were tested in different scenarios. It is demonstrated that the Tenengrad image estimation function (IEF) is suitable for an instant and accurate auto-focus process of the telescope. Furthermore, we implemented sampling and dynamic adaptive focusing window (ES-DAFW) methods with the Tenengrad IEF to enhance the sensitivity and accuracy of the auto-focus process. The experimental results showed that our ES-DATW method can provide more accurate results in less time for the auto-focus process compared to the conventional approaches, especially for a sparse image. These results promise significant applications to the auto-focusing of other telescopes with image quality assessment.

https://www.mdpi.com/2076-3417/10/2/658/pdf

Chunping Yang

Papers

Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

Infrared Small Target Detection via Nonconvex Tensor Fibered Rank Approximation

Infrared small target detection via self-regularized weighted sparse model

Cirrus detection based on RPCA and fractal dictionary learning in infrared imagery

Accurate and Rapid Auto-Focus Methods Based on Image Quality Assessment for Telescope Observation