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摘要:
为了更好地保留多光谱去马赛克图像中的高频信息，本文提出了一种基于改进引导滤波器的多光谱图像去马赛克方法。首先，基于自回归模型对相邻像素点间的强相关性进行建模，在每个像素处渐进估计其模型参数，通过最小化局部窗口内的估计误差，得到最优估计值来插值采样密集波段G，并生成高质量的引导图像；然后，引入加窗固有变分系数到惩罚因子中，得到具有边缘感知能力的加权引导滤波器并重建其余稀疏采样波段。最后，使用CAVE数据集和TokyoTech数据集进行仿真。实验结果表明：相较于主流的5波段多光谱图像去马赛克方法，本方法重建图像的峰值信噪比和结构相似度在CAVE数据集和TokyoTech数据集上分别提高了3.40%，2.02%，1.34%，0.30%和6.11%，5.95%，2.28%，1.42%，且更好地保留了原始图像的局部结构和颜色信息，减少了边缘伪影和噪声现象的出现。
Abstract:In order to better preserve high-frequency information in demosaicing multispectral images, we propose a new demosaicing method for multispectral images based on an improved guided filter. Firstly, the strong correlation between adjacent pixels based on the autoregressive model is constructed, gradually estimates the model parameters at each pixel, and the optimal estimation value is obtained by minimizing the estimation error in the local window, interpolates the sampling dense band G, and generates high-quality guide images. The windowed intrinsic variation coefficient is then introduced into the penalty factor to obtain a weighted guide filter with edge sensing ability and to reconstruct the remaining sparse sampling bands. Finally, the CAVE dataset and the TokyoTech dataset are used for simulation. The experimental results show that compared with the mainstream five-band multispectral image demosaicing method, the peak signal-to-noise ratio and structure similarity of the reconstructed image in the CAVE dataset and the TokyoTech dataset are improved by 3.40%, 2.02%, 1.34%, 0.30% and 6.11%, 5.95%, 2.28%, 1.42%, respectively. The local structure and color information of the original image are also better preserved, and the edge artifacts and noise are reduced.
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图 15波段MSFA(a)二叉树分裂过程及(b)排列模式
Figure 1.(a) Binary tree splitting process and (b) arrangement of Five-band MSFA
图 6不同算法Balloons场景重建图像对比
Figure 6.Comparison of Balloons images reconstructed by different algorithms
图 7不同算法CD场景重建图像对比
Figure 7.Comparison of CD images reconstructed by different algorithms
图 8不同算法Party场景重建图像对比
Figure 8.Comparison of party images reconstructed by different algorithms
表 1CAVE数据集上3种方法的客观评价指标
Table 1.Objective evaluation metrics of the three methods on the CAVE dataset
CAVE sRGB PSNR/dB sRGB SSIM CIEDE 2000 GF APMID Pro GF APMID Pro GF APMID Pro Balloons 41.62 42.68 43.11 0.9859 0.9916 0.9936 1.18 1.06 0.99 Clay 37.33 37.63 38.69 0.8758 0.8817 0.8852 1.07 0.94 0.89 Beers 41.57 42.18 43.52 0.9816 0.9868 0.9894 1.25 1.28 1.07 Lemons 42.91 42.87 42.91 0.9749 0.9805 0.9822 1.11 1.08 1.03 Peppers 42.14 42.08 42.52 0.9715 0.9801 0.9805 0.89 0.78 0.73 Feathers 35.64 35.94 35.99 0.9413 0.9593 0.9614 2.30 2.10 2.04 Flowers 38.93 41.18 42.50 0.9523 0.9778 0.9824 1.26 0.91 0.83 Paints 36.16 34.88 36.32 0.9696 0.9729 0.9797 2.40 2.43 2.17 Apples 45.24 45.10 45.68 0.9838 0.9875 0.9886 0.77 0.75 0.70 Toys 38.83 41.29 42.77 0.9666 0.9845 0.9891 1.24 0.90 0.80 Avg 40.04 40.58 41.40 0.9603 0.9703 0.9732 1.35 1.22 1.13 
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表 2TokyoTech数据集上3种方法的客观评价指标
Table 2.Objective evaluation metrics of the three methods on the TokyoTech dataset
TokyoTech sRGB PSNR/dB sRGB SSIM CIEDE 2000 GF APMID Pro GF APMID Pro GF APMID Pro Butterfly 37.53 38.95 40.44 0.9596 0.9678 0.9810 1.60 1.42 1.17 Butterfly3 38.42 42.94 41.99 0.9487 0.9777 0.9793 1.37 0.91 0.84 Butterfly4 40.63 40.73 42.37 0.9691 0.9590 0.9827 1.12 1.23 0.87 CD 32.20 32.78 32.87 0.9450 0.9580 0.9629 1.97 1.72 1.65 Character 37.74 37.45 38.14 0.9673 0.9736 0.9835 1.83 1.94 1.73 Cloth 34.18 35.00 35.87 0.9321 0.9481 0.9573 3.34 3.22 2.75 Color 39.22 38.62 41.36 0.9782 0.9630 0.9895 1.74 2.00 1.47 Colorchart 42.83 44.79 47.80 0.9819 0.9847 0.9941 0.92 0.77 0.55 Fan2 32.68 33.29 34.09 0.9257 0.9426 0.9629 2.63 2.34 2.04 Party 32.79 33.45 35.78 0.9366 0.9509 0.9693 2.06 1.66 1.36 Avg 36.82 37.80 39.07 0.9544 0.9625 0.9762 1.86 1.72 1.44 下载:
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表 3不同方法在两种数据集上的运行时间
Table 3.Running times of different methods on the two datasets
(s) 数据集 GF APMID Pro CAVE 1.49 0.71 1.33 TokyoTech 1.36 0.56 1.29 下载:
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