Accurate and rapid detection of small targets against complex background is a fundamental requirement of various computer vision systems. This work is the first attempt to apply the superpixel segmentation technology to the field of low resolution infrared small target detection in the extremely complex backgrounds. The main contributions are as follows. First of all, the simple linear iterative cluster (SLIC) algorithm is utilized to accurately classify the raw infrared image into three components: outlier superpixels, stable background superpixels and target superpixels, which appropriately aggregate similar background components as the basic unit of subsequent processing. In SLIC, an optional range of superpixels numbers is specified to robustly implement superpixel segmentation strategy on low resolution infrared images. Second, an outlier superpixel masking (OSM) model is proposed to perform accurate identification and masking of the outlier superpixels with highly heterogeneous backgrounds, thus minimizing false alarm rate. Specially, a 2D Gaussian filter matching the target distribution is introduced to blur the remaining boundary and pixel-sized noises with high brightness (PNHB) while maximizing the signal-to-noise ratio (SNR). Finally, a singular value truncation strategy with entropy weighted sparse factor (SVT-EW) is proposed to implement the final target extraction, which assigns specific sparsity weights for small infrared targets. SVT-EW effectively resolves the background residuals in gray-based threshold segmentation, and therefore generates precise target detection results. Extensive experimental results on fourteen extremely complex infrared natural scenes validate the superiority of the proposed method over the state-of-the-arts with respect to robustness and real-time performance.
A sparse infrared small target detection algorithm based on local spatial gradient peaks is proposed to deal with the problem of slow running speed and edge sensitivity in low-rank decomposition methods.The detection steps are as follows. In the first step, the image expansion operation is used for preprocessing. We use the circular structure element to sharpen the edges of targets and smooth the background noise. Then, the saliency gradient features of the target local region are applied to calculate the overlapping gradient information of the image after expansion. The local area with a larger gradient peak is located in the original image, and the selected local area is considered to be the region of interest with candidate targets. Finally, we use the advanced accelerated proximal gradient algorithm to perform matrix decomposition in the extracted local regions of interest to extract sparse infrared small targets. Extensive experimental results under real scenarios illustrated that compared with the baseline low-rank sparse decomposition method, the proposed approach runs faster and shows superior detection performance in the comprehensive evaluation index.
Local contrast measure (LCM) has been proved to be an effective method for infrared small target detection. However, the detection performance of LCM decreases dramatically when the background contains strong edges and pixelsized noises with high brightness (PNHB). Based on the analysis of the inherent causes of the poor performance of LCM in extremely complex backgrounds, this study presents an effective LCM with an iterative error. The contribution is as follows: first, the two-dimensional least mean square (TDLMS) filter with an adaptive parameter is applied to suppress the background clutters roughly in each multiscale window. Then, the partial maximum pixel mean is applied to the LCM to optimise the subblock statistical parameters, which achieves excellent strong edges suppression performance. Finally, the iteration error generated by TDLMS and the sub-block weight matrix is updated alternately to further optimise the statistical parameters of the contrast measure to make it more effective in suppressing PNHB. Experimental results demonstrate that the proposed approach is not only superior to the contrast methods in terms of high detection efficiency and low false alarm rate but also has satisfactory adaptability under extremely complex backgrounds.
Infrared small target detection is a crucial and challenging topic for various applications. In recent years, the spectrum scale space (SSS) algorithm has shown considerable potential in the field of target detection. However, the SSS algorithm is prone to high false alarm rates in infrared small target detection scenarios with complex background. This paper proposes an improved SSS (ISSS) algorithm via precise feature matching and scale selection strategy for efficient infrared small target detection, which includes background suppression, feature matching and optimal scale selection three stages. In the background suppression stage, a matrix decomposition method named inexact augmented Lagrange multiplier (IALM) algorithm is used to extract the sparse image matrix from the original image as the target foreground image. In the feature matching stage, the 16 elaborate Gaussian kernel functions convolve with the the amplitude spectrum of target foreground image to generate 16 scale saliency maps that precisely match the feature of small targets. In the optimal scale selection stage, a few proper candidate scale maps are screened out according to the difference between the pixel values of the target area and the background clutters, in which the target area was more highlighted, and the scale map corresponding to the maximum value of local information entropy of the candidate saliency map is the final detection result map. We mainly made three contributions: First, IALM algorithm is utilized as a preprocessing step, and we have verified it is indispensable in eliminating most backgrounds with self-correlation property. Second, an elaborate scale division strategy is proposed to obtain multi-scale saliency maps that match the feature of infrared small targets precisely. Third, the gray value difference and the maximum value of local information entropy are defined and used as the judgment criteria for optimal scale selection. Extensive experimental results demonstrate that the proposed method outperforms state-of-the-art techniques, especially on infrared images with thick clouds and high-brightness buildings. INDEX TERMS Infrared small target detection, improved spectrum scale space, matrix decomposition.
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