Author Affiliations
1Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China2Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Shanghai 200083, China3University of Chinese Academy of Sciences, Beijing 100049, Chinashow less
Fig. 1. Schematic diagram of the micro-polarizer array
Fig. 2. Flow chart of our algorithm
Fig. 3. Denoising model of the DoFP polarization image
Fig. 4. DoFP polarization image obtained by simulation. (a) DoFP; (b) 0°; (c) 45°; (d) 90°; (e) 135°
Fig. 5. Test images. (a) Fabrics; (b) leaves; (c) macbeth classic; (d) macbeth enhancement; (e) painting; (f) potery
Fig. 6. Potery images before and after denoising. (a) Original image; (b) demosaicing image; (c) noisy image; (d) denoised image
Fig. 7. Denoising results under different σ. (a) PSNR; (b) SSIM
Fig. 8. Denoising results of different algorithms. (a) Original image; (b) demosaicing image; (c) noisy image; (d) PCA algorithm; (e) K-SVD algorithm; (f) BM3D algorithm; (g) our algorithm
Fig. 9. Denoising results of different algorithms for image macbeth enhancement. (a) PSNR; (b) SSIM
Fig. 10. Denoising result of real images by different algorithms. (a) Original image; (b) non-uniformity corrected image; (c) PCA algorithm; (d) K-SVD algorithm; (e) BM3D algorithm; (f) our algorithm
Image | S0 | S1 | S2 | DoLP |
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Demosaicing image | PSNR /dB | 46.5230 | 38.6166 | 41.6959 | 31.3399 | | SSIM | 0.9956 | 0.9512 | 0.9645 | 0.7770 | Noisy image | PSNR /dB | 35.0436 | 26.1517 | 26.2277 | 5.9415 | | SSIM | 0.8104 | 0.2388 | 0.2196 | 0.1175 | Denoised image | PSNR /dB | 41.4074 | 37.1018 | 39.5708 | 25.9634 | | SSIM | 0.9809 | 0.9060 | 0.9215 | 0.5934 |
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Table 1. Stokes vector images and DoLP images before and after denoising
Algorithm | PCA | K-SVD | BM3D | Ours |
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Time /s | 5.9247 | 34.2411 | 0.3866 | 0.8869 |
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Table 2. Average running time of different algorithms
Algorithm | Fabrics | Leaves | Macbethclassic | Macbethenhancement | Painting | Potery |
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Noisy image | PSNR /dB | 28.1379 | 28.1232 | 28.1315 | 28.1443 | 28.1412 | 28.1259 | | SSIM | 0.6787 | 0.4759 | 0.6143 | 0.7773 | 0.7606 | 0.5223 | PCA | PSNR /dB | 34.5693 | 37.5385 | 38.4190 | 38.1194 | 33.8570 | 37.0131 | | SSIM | 0.9095 | 0.9011 | 0.9481 | 0.9734 | 0.9390 | 0.9124 | K-SVD | PSNR /dB | 36.1498 | 39.0277 | 37.1737 | 37.7568 | 33.6660 | 38.0791 | | SSIM | 0.9403 | 0.9365 | 0.9068 | 0.9608 | 0.9334 | 0.9438 | BM3D | PSNR /dB | 33.1474 | 37.7663 | 39.3794 | 37.6404 | 32.7475 | 37.3816 | | SSIM | 0.8937 | 0.9286 | 0.9611 | 0.9668 | 0.9316 | 0.9456 | Ours | PSNR /dB | 36.9616 | 41.5592 | 43.7434 | 42.5957 | 35.2164 | 40.3545 | | SSIM | 0.9543 | 0.9778 | 0.9900 | 0.9940 | 0.9626 | 0.9826 |
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Table 3. Denoising results of different algorithms
Algorithm | S0 | S1 | S2 | DoLP |
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Demosaicing | PSNR /dB | 49.4055 | 41.9600 | 43.4291 | 33.9477 | | SSIM | 0.9975 | 0.9600 | 0.9816 | 0.8999 | Noisy image | PSNR /dB | 35.1736 | 26.2301 | 26.2683 | 11.8090 | | SSIM | 0.8235 | 0.2948 | 0.3727 | 0.1553 | PCA | PSNR /dB | 41.8979 | 35.6964 | 35.6833 | 18.5476 | | SSIM | 0.9723 | 0.8111 | 0.8540 | 0.4134 | K-SVD | PSNR /dB | 41.6852 | 34.8183 | 33.6769 | 12.9843 | | SSIM | 0.9744 | 0.7815 | 0.8142 | 0.4154 | BM3D | PSNR /dB | 40.9549 | 36.5001 | 35.7293 | 20.7019 | | SSIM | 0.9689 | 0.8720 | 0.8902 | 0.5247 | Ours | PSNR /dB | 43.0844 | 39.9698 | 39.6139 | 27.0429 | | SSIM | 0.9847 | 0.9305 | 0.9527 | 0.7623 |
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Table 4. Denoising results of different algorithms on macbeth enhancement images