The robust detection of small targets is one of the key techniques in infrared search and tracking applications. A novel small target detection method in a single infrared image is proposed in this paper. Initially, the traditional infrared image model is generalized to a new infrared patch-image model using local patch construction. Then, because of the non-local self-correlation property of the infrared background image, based on the new model small target detection is formulated as an optimization problem of recovering low-rank and sparse matrices, which is effectively solved using stable principle component pursuit. Finally, a simple adaptive segmentation method is used to segment the target image and the segmentation result can be refined by post-processing. Extensive synthetic and real data experiments show that under different clutter backgrounds the proposed method not only works more stably for different target sizes and signal-to-clutter ratio values, but also has better detection performance compared with conventional baseline methods.

Infrared Patch-Image Model for Small Target Detection in a Single Image

Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

Effective detection of small targets plays a pivotal role in infrared (IR) search and track applications for modern military defense or attack Consequently, an effective small IR target detection algorithm based on a novel local contrast measure (NLCM) is proposed in this letter Initially, difference of Gaussian band-pass filter is employed to enhance target and suppress background clutter Then, a segmentation operation is implemented to obtain IR local regions of fixed size larger than general IR small target size Finally, the salient map is obtained using the NLCM, and an adaptive threshold is applied to extract the target region Experimental results on two real sequences show that the proposed method has better detection performance compared with conventional baseline methods

Effective Infrared Small Target Detection Utilizing a Novel Local Contrast Method

In the present scenario, retinal image processing is toiling hard to get an efficient algorithm for de-noising and segmenting the blood vessel confined inside the closed curvature boundary. On this ground, this study presents a hybrid active contour model with a novel preprocessing technique to segment the retinal blood vessel in different fundus images. Contour driven black top-hat transformation and phase-based binarisation method have been implemented to preserve the edge and corner details of the vessels. In the proposed work, gradient vector flow (GVF)-based snake and balloon method are combined to achieve better accuracy over different existing active contour models. In the earlier active contour models, the snake cannot enter inside the closed curvature resulting loss of tiny blood vessels. To circumvent this problem, an inflation term F
 inf(balloon)
 with GVF-based snake is incorporated together to achieve the new internal energy of snake for effective vessel segmentation. The evaluation parameters are calculated over four publically available databases: STARE, DRIVE, CHASE, and VAMPIRE. The proposed model outperforms its competitors by calculating a wide range of proven parameters to prove its robustness. The proposed method achieves an accuracy of 0.97 for DRIVE & CHASE and 0.96 for STARE & VAMPIRE datasets.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2018.5413

Robust retinal blood vessel segmentation using hybrid active contour model

This paper presents an algorithm for detecting the small infrared target under complex background. An method, Local Mutation Weighted Information Entropy (LMWIE), is proposed to suppress background. Then, enhance targets’ gray value by calculating the local energy. For the problem that the gray value of noises is enhanced with the gray value improvement of targets, image segmentation bases on the adaptive threshold. Experiment results indicate that it is a robust and effective small target detection algorithm.

Infrared Small Target Detection under Complex Background

Top-hat transformation has been widely used for infrared small target detection, but it is sensitive to the clutter and may destroy the details of the image, which make it an ineffective way for small target detection. To detect the infrared small target simply and efficiently, a new Top-hat transformation based on contour structuring element is proposed and then modified according to the property of the target region to enhance and detect the infrared small target in this paper. Experimental results indicated that the proposed method was efficient under the conditions of heavy clutter and dim target intensity.

Modified top-hat transformation based on contour structuring element to detect infrared small target

Multi-target tracking is a crucial but complex problem in small target tracking applications, especially when large number of false alarms existed. In order to simplify the multi-target tracking, a novel simple algorithm is presented by using motion properties to reduce false alarms, which is implemented through mathematical morphology and multi-frame cumulation. Firstly, the consecutive images are dilated and cumulated according to the motion properties of the small targets, thus generate the cumulating image. Secondly, most of the false alarms are reduced from the cumulating image through mathematical morphological operation. Finally, the resulting consecutive images including targets and very few false alarms are restored from the processed cumulating image and the original consecutive binary images. Experiment results illustrated that most of the false alarms were excluded and the small targets were easier to be identified, which largely decreased the computational time of target tracking algorithm, and thereby decreasing the complexity of the small targets tracking.

Xie Yongchun

Papers

Modified top-hat transformation based on contour structuring element to detect infrared small target

Simplification of small target tracking by using motion properties to reduce false alarms in clutter