Laser & Optoelectronics Progress, Volume. 57, Issue 18, 181001(2020)
Denoising Method for Improving Detection Accuracy of Point Source Method by MTF
Figures & Tables(17)
Fig. 2. Ideal two-dimensional Gaussian distribution. (a) Three-dimensional view; (b) sectional view
Fig. 4. Influence of partition parameter P on MSE of MTF (a=0.8). (a) MSE of image with noise standard deviation of 30 under various denoising methods; (b) relationship between parameter P and MSE under different noise standard deviations
Fig. 6. Influence of smoothing parameter a on MSE of MTF (P=0.3). (a) MSE of image with noise standard deviation of 30 under various denoising methods; (b) relationship between parameter a and MSE under different noise standard deviations
Fig. 7. Sequence of out-of-focus images. (a) image 1; (b) image 2; (c) image 3; (d) image 4; (e) image 5; (f) image 6
Fig. 8. Three-dimensional images and two-dimensional profiles of different denoising methods (noise standard deviation is 20). (a) Original point source image; (b) add noise point source image; (c) mean filtering; (d) median filtering; (e) wavelet filtering; (f) proposed method
Fig. 13. Sequences of captured out-of-focus source images. (a) -2.5 mm; (b) -2.0 mm; (c) -1.5 mm; (d) -1.0 mm; (e) 0 mm; (f) 1.0 mm; (g) +1.5 mm; (h) +2.0 mm; (i) +2.5 mm
Fig. 14. MTF measured after different denoising methods (defocused amount: -2.5 mm)
Table 1. Performance comparison between proposed denoising method and traditional filtering methods
View tableTable 1. Performance comparison between proposed denoising method and traditional filtering methods
Standard deviation of noise Performance Noise image Proposed method Mean filtering method Medium filtering method Wavelet filtering method MSE 0.0107 0.0023 0.0054 0.0060 0.0047 5 PSNR 39.5126 41.5314 41.0826 40.9626 41.1824 SSIM 0.9502 0.9940 0.9829 0.9789 0.9850 MSE 0.0205 0.0019 0.0110 0.0133 0.0087 10 PSNR 32.6774 35.9853 35.1366 35.2308 35.4256 SSIM 0.7662 0.9814 0.9367 0.9236 0.9571 MSE 0.0270 0.0040 0.0155 0.0173 0.0127 15 PSNR 28.2794 32.4906 31.6207 32.0162 31.9966 SSIM 0.5133 0.9616 0.8688 0.8452 0.9209 MSE 0.0321 0.0061 0.0193 0.0203 0.0159 20 PSNR 25.1575 30.0172 29.1058 29.9099 29.5053 SSIM 0.3225 0.9391 0.7883 0.7601 0.8740 MSE 0.0330 0.0096 0.0210 0.0223 0.0186 25 PSNR 22.8968 28.0722 27.1704 28.4650 27.5833 SSIM 0.2119 0.9148 0.7068 0.6848 0.8236 MSE 0.0338 0.0098 0.0230 0.0242 0.0200 30 PSNR 21.1746 26.4851 25.5809 27.3332 25.9853 SSIM 0.1484 0.8875 0.6280 0.6173 0.7682 MSE 0.0262 0.0056 0.0159 0.0172 0.0134 Mean value PSNR 28.2831 32.4303 31.6162 32.3196 31.9464 SSIM 0.4854 0.9464 0.8186 0.8016 0.8881
Table 2. Parameters of experimental devices
View tableTable 2. Parameters of experimental devices
Experimental device Parameter Value Light source Center wavelength /nm 550 CMOS detector Cell size /mm 0.00345 Lens Focal length /mm 50 Posterior intercept/mm 12.4
Table 3. Comparison between proposed denoising method and traditional filtering methods
View tableTable 3. Comparison between proposed denoising method and traditional filtering methods
Parameter Noise image Mean filtering method Medium filtering method Wavelet filtering method Proposed method MTF curve area 0.0563 0.0547 0.0706 0.0572 0.1403 PSNR Inf 36.2199 36.4075 36.8810 32.1785 SSIM 1 0.8415 0.8337 0.8318 0.8283
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Lixuan Chen, Peng Rao, Hanlu Zhu, Yingying Sun, Liangjie Jia. Denoising Method for Improving Detection Accuracy of Point Source Method by MTF[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181001
Paper Information
Category: Image Processing
Received: Nov. 26, 2019
Accepted: Jan. 6, 2020
Published Online: Sep. 2, 2020
The Author Email: Rao Peng (Peng_rao@mail.sitp.ac.cn)
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