Infrared small target detection method based on nonlinear local filter
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摘要: 為提高復雜環境下紅外小目標的檢測效率,將圖像分為平坦區域、邊緣區域和小目標區域三種區域,并針對三種成分的特點,提出基于拉普拉斯金字塔的非線性局部濾波檢測方法.首先將圖像進行高斯金字塔分解,將高斯低通金字塔與原圖像尺寸匹配后,相減并進行閾值操作,抑制平坦區域;其次將標記像素灰度值與其周圍環域均值的最小差作為指標,濾除邊界區域;最后將非線性局部濾波結果生成相應的拉普拉斯金字塔各層系數,重構得到高對比度的檢測圖像,利用鄰域特點剔除孤立噪聲點并通過簡單閾值標記紅外小目標.實驗結果表明:與現有其他算法相比,該檢測算法能夠對復雜背景有效抑制,檢測速度快.Abstract: In order to improve the efficiency of infrared small target detection against complex background, the image was decomposed into three regions flat region, edge region and small target region. A method of nonlinear local filter detection using the Laplaclan pyramid was presented based on each character of the three components. Firstly, Gaussian pyramids were built for the image, each level was subtracted from the original image with matching size, and the flat region was restrained by simple threshold operation. Secondly, the minimum difference between the marked pixel gray value and the mean value of the hollow annular region was used as quota to filter out the edge region. At last, each layer coefficient of the Laplacian pyramid was generated from the results of nonlinear local filtering and then a high-contrast detection image was reconstructed. The isolated noise points were removed based on the character of the neighborhood and the infrared small target was marked by simple threshold operation. Compared with other existing methods, the experimental results show that this method can effectively restrain complex background and the detection speed is fast.
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Key words:
- infrared image processing /
- target detection /
- Laplacian pyramid /
- nonlinear filtering /
- local filter
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