Infrared background suppression algorithm based on guided filtering and fuzzy algorithm
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摘要: 为了减少背景对红外小目标检测结果的影响，同时降低检测虚警率，采用了基于引导滤波和模糊算法的红外背景抑制算法，利用非下采样轮廓波多尺度、多方向的分解机制，将红外序列图像分解为低通子带和带通子带；再利用引导滤波对低通子带处理，以平滑图像、抑制噪声、增强背景细节；带通子带则采用模糊算法处理，实现目标和残留背景分离；最后将各子带图像通过非下采样轮廓波逆变换，得到了背景抑制图像。结果表明，该方法可以将均方误差降至5~10，有效抑制了背景，突出了目标。该研究为提高复杂背景下的红外小目标检测精度提供了支持。Abstract: In order to reduce the influence of background on detection results of infrared small targets and reduce false alarm rate, infrared background suppression algorithm based on guidance filter and fuzzy algorithm was adopted. The infrared image was decomposed into low-pass band and band-pass band by using multi-scale and multi-direction decomposition mechanism of non sampled contour. The guided filter was used to process low-pass sub-band to smooth images, suppress noise and enhance background details. Band-pass sub-band was processed by fuzzy algorithm to seperate the target from the residual background. Background suppression image was obtained by changing subband images through non subsampled contour inversion. The results show that, the method can reduce mean square error to 5~10, and effectively suppress the background and highlight the target. This study provides the support for improving the detection accuracy of infrared small targets in complex background.
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Figure 1. Decomposition structure of non-subsampled pyramid filter bank after iterations
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Figure 5. Experimental results
a, g, m-infrared image b, h, n-top-hat c, i, o-GIF d, j, p-RPCA e, k, q-NSCT f, l, r-algorithm of this paper
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Table 1 Comparison among the experimental results
Fig. 5m Fig. 5g Fig. 5a MSE PSNR MSE PSNR MSE PSNR top-hat 16.6446 32.8435 13.0185 36.7370 12.3329 36.1820 GIF 15.5397 33.1418 13.0016 36.7426 12.0448 36.2847 RPCA 10.5324 34.8309 6.6929 36.8000 6.2712 37.0826 NSCT 25.6984 30.9572 10.8570 37.1255 5.8153 39.4470 algorithm of this paper 9.9399 35.0824 5.8548 37.3811 5.1063 40.0116 
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