Chinese Optics, Volume. 17, Issue 6, 1329(2024)
Broad-band co-phase detection based on denoising convolutional neural network
Fig. 1. Schematic diagram of circular aperture diffraction between submirrors
Fig. 2. Theoretical diffractogram of circular aperture with mask radius
Fig. 4. Corr2 image under the influence of 20 dB additive white Gaussian noise
Fig. 7. The change process of the loss function and learning rate during DnCNN training
Fig. 8. Plots of the noise reduction effect of different methods for Gaussian noise for four sets of submirror piston errors of −0.2, 0, 0.2, and 2 μm. (a) Clear images; (b) noisy images; (c) images after noise reduction by BM3D network; (d) images after noise reduction by WNNM network; (e) images after noise reduction by DnCNN
Fig. 10. The denoising effect of different methods on Poisson noise for four sets of submirror piston errors of −0.2 μm, 0, 0.2 μm, and 2 μm (from left to right). (a) Clear images; (b) noisy images; (c) images denoised by BM3D network; (d) images denoised by DnCNN
Fig. 13. Camera direct images (a) without piston error (b) with piston error
Fig. 14. Comparison of denoise effect after image enhancement. (a) (b) Original noise-containing images; (c) (d) images after denoising
Table 1. SSIM values between images containing Gaussian noise with four sets of submirror piston errors before and after noise reduction and clear noise-free images
View tableView in ArticleTable 1. SSIM values between images containing Gaussian noise with four sets of submirror piston errors before and after noise reduction and clear noise-free images
piston SSIM值 BM3D WNNM DnCNN −0.2 μm 0.3527 0.2049 0.9747 0 0.2680 0.1376 0.9811 0.2 μm 0.3639 0.2109 0.9762 2 μm 0.3959 0.2294 0.9810 SSIM均值 0.3451 0.1957 0.9783
Table 2. SSIM values between pre- and post-noise reduction images with four sets of submirror piston errors and clear noise-free images when PSNR in the range of 40 dB to 20 dB
View tableView in ArticleTable 2. SSIM values between pre- and post-noise reduction images with four sets of submirror piston errors and clear noise-free images when PSNR in the range of 40 dB to 20 dB
piston=-0.2 μm piston=0 piston=0.2 μm piston=2 μm PSNR before after before after before after before after 20 0.2875 0.9747 0.2610 0.9811 0.2892 0.9762 0.2954 0.9810 24 0.4794 0.9766 0.4669 0.9954 0.4858 0.9785 0.4942 0.9811 28 0.6992 0.9753 0.6798 0.9962 0.7001 0.9769 0.6981 0.9824 32 0.8536 0.9739 0.8387 0.9962 0.8537 0.9738 0.8519 0.9785 36 0.9349 0.9735 0.9321 0.9965 0.9354 0.9739 0.9366 0.9761 40 0.9731 0.9726 0.9714 0.9966 0.9726 0.9728 0.9735 0.9750 AVG 0.7046 0.9744 0.6917 0.9937 0.7061 0.9754 0.7083 0.9790
Table 3. SSIM values between images containing Poisson noise with four sets of submirror piston errors before and after noise reduction and clear noise-free image
View tableView in ArticleTable 3. SSIM values between images containing Poisson noise with four sets of submirror piston errors before and after noise reduction and clear noise-free image
piston=−0.2 μm piston=0 piston=0.2 μm piston=2 μm 平均值 BM3D 0.9114 0.8966 0.9334 0.9046 0.9115 DnCNN 0.9998 0.9998 0.9997 0.9998 0.9998
Table 4. SSIM values of pre- and post-noise reduction images and clear noise-free images with PSNR in the range of 30 dB to 10 dB for four sets of submirror piston errors
View tableView in ArticleTable 4. SSIM values of pre- and post-noise reduction images and clear noise-free images with PSNR in the range of 30 dB to 10 dB for four sets of submirror piston errors
piston=-0.2 μm piston=0 piston=0.2 μm piston=2 μm PSNR before after before after before after before after 10 0.0726 0.9973 0.0579 0.9970 0.0761 0.9973 0.0838 0.9973 14 0.2046 0.9995 0.2032 0.9996 0.1985 0.9995 0.3131 0.9996 18 0.5811 0.9995 0.5337 0.9993 0.5453 0.9995 0.6148 0.9981 22 0.7555 0.9997 0.8255 0.9998 0.7599 0.9995 0.7926 0.9997 26 0.8767 0.9999 0.9125 0.9999 0.9406 0.9998 0.9617 0.9999 30 0.9543 0.9998 0.9781 0.9998 0.9496 0.9999 0.9307 0.9997 AVG 0.5741 0.9993 0.5852 0.9992 0.5783 0.9993 0.6161 0.9991
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Bin LI, Yin-ling LIU, A-kun YANG, Mo CHEN. Broad-band co-phase detection based on denoising convolutional neural network[J]. Chinese Optics, 2024, 17(6): 1329
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Received: Apr. 28, 2024
Accepted: Jul. 12, 2024
Published Online: Jan. 14, 2025
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