Photonics Research, Volume. 9, Issue 5, B210(2021)
Imaging through unknown scattering media based on physics-informed learning
Figures & Tables(15)
Fig. 1. Speckle statistical characteristics analysis of the same object corresponding to different testing diffusers. (a) First row and second row are the speckle autocorrelation of the object within or exceeding the OME range, the third row is the cross-correlation with D1, respectively. (b)–(d) Intensity values of the white dash lines in the first, second, and third rows of (a), respectively. The color bar represents the normalized intensity. Scale bars: 875.52 µm.
Fig. 2. Schematic of the physics-informed learning method for scalable scattering imaging.
Fig. 3. Experimental setup for the scalable imaging. Different diffusers are employed to obtain speckle patterns with different scattering scenes. The OME range of this system is also measured by calculating the cross-correlation coefficient [21]. See Appendix
Fig. 4. Testing results for generalization reconstruction of Group 1. Scale bars: 264.24 µm.
Fig. 5. Testing results for generalization reconstruction of Group 2. Scale bars: 264.24 µm.
Fig. 6. Testing results for generalization reconstruction of Group 3. Scale bars: 264.24 µm.
Fig. 7. Testing results for generalization reconstruction of Group 4. Scale bars: 820.8 µm.
Fig. 8. Generalization results for a single-character object with different scales and the scale of FOV is defined as the FOV/OME times. (a), (b) Results with different amounts of training diffusers, which are trained with one diffuser and three diffusers, respectively. (c) Reconstruction results with different scales and corresponding ground truth (GT).
Fig. 9. Comparison results without or with this pre-processing step for imaging through an unknown diffuser. Three ground glasses are selected as the training diffusers and another diffuser for testing.
Fig. 10. Results with different number of speckles via the physics-informed learning method. Three ground glasses are selected as the training diffusers and another diffuser for testing.
Fig. 11. Generalization results of imaging exceeding OME range with different complexity objects.
Table 1. Quantitative Evaluation Results of the Objects within OME
View tableView in ArticleTable 1. Quantitative Evaluation Results of the Objects within OME
Type Training Set Testing Set MAE SSIM PSNR (dB) Group 1 D1 Seen objects 0.0190 0.8477 23.41 D1 Unseen objects 0.0288 0.8248 19.86 D1, D2, D3 Seen objects 0.0033 0.9744 40.56 D1, D2, D3 Unseen objects 0.0271 0.8088 20.58 Group 2 D1 Seen objects 0.0257 0.8608 20.75 D1 Unseen objects 0.0548 0.6929 16.56 D1, D2, D3 Seen objects 0.0072 0.9190 38.60 D1, D2, D3 Unseen objects 0.0455 0.7678 16.76 Group 3 D1 Seen objects 0.1166 0.4672 15.59 D1 Unseen objects 0.1234 0.5351 14.98 D1, D2, D3 Seen objects 0.0323 0.8523 23.82 D1, D2, D3 Unseen objects 0.0534 0.7410 20.67
Table 2. Quantitative Evaluation Results of Objects Extending the FOV 1.2 Times
View tableView in ArticleTable 2. Quantitative Evaluation Results of Objects Extending the FOV 1.2 Times
Type Training Set Testing Set MAE SSIM PSNR (dB) Group 4 D1 Seen objects 0.0359 0.8439 17.53 D1 Unseen objects 0.0432 0.6968 17.12 D1, D2, D3 Seen objects 0.0085 0.9431 30.55 D1, D2, D3 Unseen objects 0.0309 0.8706 18.90
Table 3. Objective Indicators with Different Number of Speckles via the Physics-Informed Learning Method
View tableView in ArticleTable 3. Objective Indicators with Different Number of Speckles via the Physics-Informed Learning Method
Number of Speckles MAE SSIM PSNR (dB) 0.0054 0.9342 38.38 0.0037 0.9539 44.53
Table 4. Objective Indicators Corresponding to Fig.
11 View tableView in ArticleTable 4. Objective Indicators Corresponding to Fig.
11 Objects Network MAE SSIM PSNR (dB) OME single characters U-Net 0.0232 0.8741 21.70 OME double characters U-Net 0.0361 0.8360 18.96 OME faces U-Net 0.0673 0.6972 18.40 OME faces PDSNet-L 0.0567 0.7050 21.16
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Shuo Zhu, Enlai Guo, Jie Gu, Lianfa Bai, Jing Han. Imaging through unknown scattering media based on physics-informed learning[J]. Photonics Research, 2021, 9(5): B210
Paper Information
Special Issue: DEEP LEARNING IN PHOTONICS
Received: Dec. 2, 2020
Accepted: Feb. 21, 2021
Published Online: Apr. 19, 2021
The Author Email: Enlai Guo (njustgel@njust.edu.cn), Jing Han (eohj@njust.edu.cn)
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