Image reconstruction of snapshot multispectral camera based on an attention residual network
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摘要:
随着光谱成像技术的飞速发展，使用多光谱滤光片阵列（multispectral filter array，MSFA）采集多光谱图像的空间和光谱信息已经成为研究热点。如何利用低采样率且具有强频谱互相关性的原始数据进行重构成为制约其发展的瓶颈。本文基于一种含有全通波段的8波段4×4 MSFA，提出了一种空谱联合的多分支注意力残差网络模型。使用多分支模型对各个波段插值后的图像特征进行学习。利用本文设计的空间通道注意力模型对8个波段和全通波段的特征信息进行联合处理。该模型通过多层卷积和卷积注意力模块以及残差补偿机制，有效减小了各波段的颜色差异，增强了边缘纹理等相关特征信息。对于初步插值的全通波段和其他波段的特征信息，通过无需进行批量归一化的残差密集块对多光谱图像空间和光谱相关性进行特征学习，以匹配各个波段的光谱信息。实验结果表明，对于在D65光源下测试图像，本文所提模型的峰值信噪比、结构相似度和光谱角相似度分别较最先进的深度学习方法提升了3.46%、0.27%和6%。该方法不仅减少了伪影还获得了更多的纹理细节。
Abstract:With the rapid advancement of spectral imaging technology, the use of multispectral filter array (MSFA) to collect the spatial and spectral information of multispectral images has become a research hotspot. The uses of the original data are limited because of its low sampling rate and strong spectral inter-correlation for reconstruction. Therefore, we propose a multi-branch attention residual network model for spatial-spectral association based on an 8-band 4 × 4 MSFA with all-pass bands. First, the multi-branch model was used to learn the image features after interpolation in each band; second, the feature information of the eight bands and the all-pass band were united by the spatial channel attention model designed in this paper, and the application of multi-layer convolution and the convolutional attention module and the use of residual compensation effectively compensated the color difference of each band and enriched the edge texture-related feature information. Finally, the preliminary interpolated full-pass band and the rest of the band feature information were used for feature learning of the spatial and spectral correlations of multispectral images through residual dense blocks without batch normalization to match the spectral information of each band. Experimental results show that the peak signal-to-noise ratio, structural similarity, and spectral angular similarity of the test image under the D65 light source outperform the state-of-the-art deep learning method by 3.46%, 0.27%, and 6%, respectively. This method not only reduces artifacts but also obtains more texture details.
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图 1 配备MSFA的单传感器多光谱相机的原始图像采集与重构过程
Figure 1. Original image acquisition and reconstruction process for a single-sensor multispectral camera equipped with MSFA
图 2 多分支注意力残差网络模型框架
Figure 2. A multi-branch attention residual network model framework
图 5 D65光源下的测试图像在sRGB颜色空间中的去马赛克效果视觉对比
Figure 5. Visual comparison of the de-mosaicing effect of test images under the D65 light source in sRGB color space
图 6 测试图像在679 nm处不同场景的去马赛克误差图视觉比较
Figure 6. Visual comparison of de-mosaicing error maps of test images in different scenarios at 679 nm
图 7 测试图像CD不同波段的去马赛克误差图视觉比较
Figure 7. Visual comparison of de-mosaicing error maps in different bands of test image CD
表 1 测试图像在A光源下的PSNR、SSIM和SAM值
Table 1. PSNR, SSIM, and SAM values of the test image under the A light source
PSNR ↑ SSIM ↑ SAM ↓ WB PPID MGCC Ours WB PPID MGCC Ours WB PPID MGCC Ours balloons 39.99 43.23 45.52 45.93 0.9977 0.9988 0.9992 0.9993 4.892 3.693 3.409 3.329 beads 28.35 31.03 32.75 33.57 0.9610 0.9768 0.9776 0.9858 10.087 8.002 7.571 6.182 Egyptian 35.22 40.77 40.96 43.65 0.9908 0.9947 0.9953 0.9972 11.358 10.648 8.653 7.570 feathers 32.38 36.32 37.03 38.48 0.9908 0.9962 0.9964 0.9976 8.351 6.782 6.036 5.960 paints 32.39 36.29 35.79 38.63 0.9939 0.9973 0.9973 0.9984 6.557 5.178 4.891 4.500 pompoms 36.37 38.28 40.54 41.02 0.9953 0.9968 0.9978 0.9987 4.395 3.524 3.003 2.916 CD 36.54 38.54 39.01 39.75 0.9939 0.9953 0.9956 0.9969 4.211 4.043 3.956 4.145 Character 29.27 34.91 38.91 39.51 0.9942 0.9981 0.9989 0.9992 6.103 3.811 3.119 3.049 ChartRes 30.24 31.26 31.45 32.63 0.9931 0.9976 0.9984 0.9996 3.962 2.761 2.529 1.300 Average 33.41 36.73 37.99 39.24 0.9900 0.9946 0.9951 0.9969 6.657 5.383 4.796 4.327 
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表 2 测试图像在D65光源下的PSNR、SSIM和SAM值
Table 2. PSNR, SSIM, and SAM values of the test image under the D65 light source
PSNR ↑ SSIM ↑ SAM ↓ WB PPID MGCC Ours WB PPID MGCC Ours WB PPID MGCC Ours balloons 40.73 44.16 46.94 48.36 0.9981 0.9991 0.9995 0.9997 3.629 3.114 2.700 2.539 beads 25.75 31.28 33.21 34.06 0.9662 0.9826 0.9808 0.9898 10.316 8.324 6.957 6.243 Egyptian 39.29 42.50 42.58 44.86 0.9929 0.9875 0.9965 0.9979 10.642 8.015 7.038 6.919 feathers 32.67 36.72 37.26 39.12 0.9916 0.9931 0.9966 0.9980 7.377 5.891 5.412 4.843 paints 31.18 36.07 36.41 38.90 0.9922 0.9974 0.9887 0.9986 6.408 4.788 3.974 3.905 pompoms 36.88 38.90 41.19 42.65 0.9962 0.9978 0.9985 0.9989 4.032 3.232 2.693 2.753 CD 34.54 38.21 40.61 41.53 0.9949 0.9952 0.9961 0.9967 3.209 2.942 2.772 2.726 Character 28.88 34.90 39.63 39.87 0.9936 0.9984 0.9994 0.9996 6.867 4.100 3.108 2.907 ChartRes 29.34 29.65 32.54 33.21 0.9951 0.9958 0.9969 0.9971 2.840 2.290 1.561 1.324 Average 33.25 36.93 38.93 40.28 0.9912 0.9941 0.9947 0.9974 6.147 4.744 4.023 3.795
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表 3 测试图像在F12光源下的PSNR、SSIM和SAM值
Table 3. PSNR, SSIM, and SAM values of the test image under the F12 light source
PSNR ↑ SSIM ↑ SAM ↓ WB PPID MGCC Ours WB PPID MGCC Ours WB PPID MGCC Ours balloons 36.68 37.83 38.60 38.91 0.9937 0.9952 0.9961 0.9989 5.540 5.640 5.537 5.955 beads 25.84 27.65 27.67 29.92 0.9390 0.9566 0.9673 0.9720 14.958 13.669 13.532 11.424 Egyptian 36.23 37.46 38.23 39.47 0.9825 0.9857 0.9862 0.9913 6.150 5.740 4.763 3.795 feathers 30.61 32.76 33.51 34.70 0.9829 0.9887 0.9889 0.9928 11.526 11.194 11.251 10.897 paints 28.02 30.89 31.98 33.93 0.9761 0.9871 0.9912 0.9939 9.801 9.440 9.415 9.113 pompoms 32.17 32.81 33.92 33.97 0.9874 0.9889 0.9907 0.9909 6.776 6.355 6.150 5.936 CD 35.58 35.99 36.26 37.06 0.9869 0.9872 0.9898 0.9988 7.934 6.691 5.957 6.622 Character 26.11 28.86 32.33 32.64 0.9824 0.9908 0.9954 0.9962 7.794 6.881 6.835 6.767 ChartRes 25.81 26.32 27.61 29.33 0.9768 0.9814 0.9826 0.9916 3.845 3.157 2.837 1.859 Average 30.78 32.28 33.34 34.44 0.9786 0.9846 0.9875 0.9918 8.258 7.640 7.364 6.929
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表 4 测试图像在3种光源下的PSNR、SSIM和SAM与不同去马赛克方法的定量比较
Table 4. Quantitative comparison of PSNR, SSIM, and SAM of test images processed by different de-mosaicing methods under three light sources
WB PPID MGCC Ours PSNR 29.37 34.56 35.69 37.81 SSIM 0.8926 0.9705 0.9902 0.9936 SAM 8.541 6.473 5.647 5.312
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表 5 不同去马赛克方法运行时间比较
Table 5. Comparison of running times of different de-mosaicing methods（Unit：ms）
WB PPID MGCC Ours CPU 254.35 2134.5 - - GPU - - 2.65 2.12
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表 6 不同网络架构的消融研究
Table 6. The ablation of different network architectures
多分支架构 残差密集块 PSNR SSIM SAM × × 36.94 0.9869 7.263 × √ 37.21 0.9907 6.726 √ √ 38.59 0.9938 6.218
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表 7 不同注意力机制方案的消融研究
Table 7. The ablation of different attention mechanism schemes
注意力机制 PSNR SSIM SAM RN 36.24 0.9804 8.352 CA 36.79 0.9873 6.316 MAM 37.28 0.9927 6.047 SCAM 38.46 0.9951 5.247
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