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Infrared small target detection is a key technique in an infrared system. In the past decade, many methods have concentrated on traditional top-hat transformation, which relies on the hand-crafted shape and value of structural elements. However, these methods are inevitably challenged by the following two aspects: first, the structural elements cannot suppress heavy clutter because the construction of structural elements is always according to the prior information of the target and unable to consider the feature of clutter. Second, adaptively extracting sufficient local feature information for background suppression is hard for the structural element. In this article, we propose an entropy-driven top-hat transformation with guided filter kernel for considering the features of both the clutters and background. First, we propose an entropy-driven top-hat transformation method with our proposed local mean entropy, which can be used to suppress clutter according to the local complex degree of clutter. Then, an adaptive structural element based on a guided filter kernel is further exploited to capture the local feature information of image for background suppression. Finally, an adaptive threshold is combined with our algorithm to achieve target detection in image sequences. The experimental results show that the proposed algorithm is not only robust for suppressing different kinds of backgrounds but can also obtain a higher value of the signal-to-clutter ratio gain and detection accuracy compared with some popular traditional baseline methods and related top-hat methods. [ABSTRACT FROM AUTHOR]

Image segmentation is a key component of image analysis, which refers to the process of partitioning the image into multiple segments. Graph cut is widely used in image segmentation by constructing a graph that the minimal cut of this graph would lead to partition the corresponding pixels of the different objects. In this paper, we reconstruct the graph cut problem as a special non-convex optimization problem instead of the traditional maximum flow problem. We extend this non-convex problem to the hypergraph method and combine it with a tensor field based on a directional bilateral filter bank to achieve segmentation in grayscale images. Accordingly, an efficient minimization algorithm is proposed to solve this non-convex problem with global convergence. Furthermore, we have selected the data of BSDS300 and BSDS500 as tests. Experimental results and evaluation index tests further demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Detecting small targets in a complex background is always carried out by suppressing the background. The top-hat transformation is mainly utilized for background suppression in target detection. Many modified top-hat transformation methods are based on the different structures of the structural elements. However, there are two limitations. One is that the structural elements cannot sufficiently consider the contrast information between the target and surrounding area to enhance the target. Another is that structural elements should be set in advance and cannot adaptively suppress complex backgrounds. In this article, our proposed top-hat transformation is designed from two cases. First, an adaptive structural element based on a guided filter kernel is proposed for capturing the local features in infrared images for background suppression. Second, a balanced ring shape is used for two structural elements of top-hat transformation, which can utilize the contrast information between the target and background for target enhancement. More than 500 infrared target images are used in our experiment. The experimental results show that our algorithm achieves better performance in signal-to-clutter ratio gain, background suppression factor, and detection accuracy when compared with recent popular baseline methods. [ABSTRACT FROM AUTHOR]