# Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
## 3 Results and Analysis
### Experimental Results  
To prove that the autocorrelation recovery of complex target speckle images could be achieved by constructing basic-element datasets derived from deep-learning models to overcome the limitations of its target shape and class, the following procedure was followed. First, the basic-element data and the Fashion-MNIST data collected under 220-mesh frosted glass were used as the training and test sets, respectively, to validate the experimental idea. The experimental results are shown in Figure 9.  
The results show that after the speckle autocorrelation with a low signal-to-noise ratio is recovered by the model, the autocorrelated images maintain a high signal-to-noise ratio in relation to its truth value autocorrelated images. The high-resolution reconstruction of the target image is achieved by combining the traditional phase-recovery algorithm.  
Figure 9: Test results under 220-mesh frosted glass. GT is the truth value of the target, GT_corr represents the truth value autocorrelation, Spec_corr is the speckle autocorrelation under 220-mesh frosted glass, Corr_rec is the depth model prediction result, Corr_Cont Curve is the comparison value between the depth model prediction result and the truth value autocorrelation result, and Phase_rec represents the phase-recovery reconstruction result.  
However, during the autocorrelation process, the model lacks the position information of the target, and the phase recovery only uses the target amplitude information. Therefore, the phase-recovery result does not reflect the position of the target. This result also verifies that the complex target autocorrelation is recovered when the basic-element training set is used.  
Based on these experimental results, the effect of autocorrelation recovery can be further verified under the condition of frosted-glass plates with different meshes as scattering media. The experimental design included crossover experiments to verify the robustness and feasibility of the model. First, the experiment was designed to use the experimental data collected from 220-mesh frosted glass as the training set to verify its recovery results for the data collected from 600-mesh frosted glass. The experimental results are shown in Figure 10:  
The autocorrelation recovery curve shows the data collected using frosted glass with low mesh as the training set. The test results using frosted glass with high mesh maintain a high signal-to-noise ratio recovery, where the _PSNR_ is 37.28 dB and _SSIM_ is 0.98. The data collected from high-mesh frosted glass was also used as the training set to verify the  
Figure 10: Results obtained using 220-mesh frosted-glass collection data as the training set and testing on 600-mesh frosted-glass collection data.  
feasibility of the experimental data collected from low-mesh frosted glass. The results are shown in Figure 11.  
Based on the phase recovery results, the target can still be recovered using different meshes. However, affected by the different characteristics of scattering media, its autocorrelation recovery results have a \(PSNR\) of 34.65 dB and \(SSIM\) of 0.94 in the validation from high mesh to low mesh. Both values decreased, but the model is still able to reconstruct clear targets using the phase recovery algorithm.  
In conclusion, the experimental results indicate that by combining deep learning with traditional methods and incorporating prior physical knowledge in the autocorrelation, high-resolution reconstruction of targets can be achieved under the influence of different noise terms, providing a new approach to reconstructing speckle images of targets in complex environments.