An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system |
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| Authors: | Xiaopeng Shao Hua Fan Guangxu Lu Jun Xu |
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| Institution: | 1. Shanghai Jiao Tong University, Institute of Image Processing & Pattern Recognition, Department of Automation, 800 Dongchuan Road, Shanghai 200240, PR China;2. University of Technology, Sydney (UTS), School of Computing and Communications, NSW 2007, Australia;1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;2. College of Computer Science, Shenyang Aerospace University, Shenyang 110136, China;1. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, Huazhong University of Science and Technology, Wuhan 430074, China;3. Key Laboratory for Image Information Processing and Intelligence Control of Education Ministry, Huazhong University of Science and Technology, Wuhan 430074, China;4. National Key Laboratory of Science and Technology on Aerospace Intelligence Control, Beijing 100101, China |
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| Abstract: | To achieve higher detection rate and lower false alarm rate in dim and small target detection, this paper proposed an improved algorithm based on the contrast mechanism of human visual system (HVS) for infrared small target detection in an image with complicated background. According to the contrast mechanism of HVS, Laplacian of Gaussian (LoG) filter is exploited to deal with the input image, which can not only suppress the background noise and clutter but also enhances the target intensity significantly. As a result it increases the contrast ratio between target and background. To further eliminate residual clutter, we process the filtered image with morphological method in all directions. True target is finally obtained by applying local thresholding segmentation to the pre-processed image. Experimental results demonstrate its superior and reliable detection performance by high detection rate and low false alarm rate. |
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