## 4 Results and Discussion  
### De-Blurring Image  
The significant impact of the attention gate on the scattering de-blurring process is demonstrated by the results presented in Figure 7. The images obtained with the Attention Unet model (D) show higher clarity and fidelity than those obtained with the standard U-Net architecture (C), as the attention mechanism effectively suppresses the scattering influence and improves the image reconstruction of the absorbing structure. Quantitative evaluation, as indicated by the Intersection Over Union (IoU) index, further supports the superiority of the gating attention approach. The IoU index of 0.908 achieved when using gating attention exceeds the IoU index of 0.831 obtained with the standard U-Net architecture. This substantial improvement demonstrates the ability of the attention gate to capture the relevant features better and reduce the impact of scattering, leading to more accurate and precise de-blurring results. The effectiveness of the attention gate can be attributed to its ability to selectively focus on informative regions and suppress the interference caused by scattering. By assigning different attention weights to different parts of the image, the attention gate improves the model's capability to accurately capture and reconstruct the absorbing structure image, even at greater depths. These findings highlight the potential of the attention gate in improving the scattering de-blurring process. Incorporating the gating attention mechanism into the U-Net architecture can significantly enhance the quality and reliability of de-blurred images, particularly in scenarios with high levels of scattering. Further exploration and optimization of the attention gate in various imaging applications hold promise for advancing the image reconstruction and de-blurring field. The effectiveness of the residual block in the scattering de-blurring process is illustrated by the results presented in Figure 8. The images obtained from the Res-UNet model show a remarkable improvement in the de-blurring outcome compared to those obtained from the U-Net model, as evidenced by the higher IoU index of 0.885. This indicates a more accurate reconstruction of the original absorber image at a depth of 15 mm, even in the presence of scattering and blurring effects. In contrast, the standard U-Net architecture yields a slightly lower IoU index of 0.831, indicating a relatively inferior de-blurring performance. The superior performance of the Residual U-Net model can be attributed to the ability of residual blocks to facilitate the propagation of gradient information effectively. By allowing for the direct flow of information through skip connections, residual blocks enable the model to capture and restore important features of the absorbing image more efficiently. Consequently, the Residual U-Net model exceeds the standard U-Net architecture in mitigating the negative impact of scattering and achieves more accurate de-blurring results. These findings demonstrate the significance of incorporating residual blocks into deep learning models for scattering de-blurring tasks. The Residual U-net model proves to be a promising approach for addressing challenges associated with image blurring in the presence of scattering media. Further investigations and optimizations can be conducted to enhance the performance of the Residual U-Net model and explore its  
Figure 7: Scattering de-blurring with and without attention gate: (**A**) transillumination image through turbid medium, (**B**) image taken through clear water, (**C**) output image from U-Net model, and (**D**) output image from Attention Unet model. potential applications in various imaging tasks, such as medical diagnostics and image analysis in turbid environments. The primary objective of this study was to obtain de-blurred images by effectively compensating for scattering effects. To accomplish this, the models employed in this approach included Attention U-Net and Attention Res-UNet.