# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 3 Results
### Comparison of Experimental Results for Reconstruction  
In the super-resolution reconstruction experiments, conventional photoacoustic image reconstruction techniques (e.g., time-reversal (TR) method) and EDSR-M were compared under different numbers of sensors. Meanwhile, the reconstruction effects of the TR method, bicubic interpolation, and deep learning-based reconstruction methods (including SRCNN, FSRCNN, VDSR, and EDSR-M) were evaluated. The reconstructed images were compared with ground truth images using PSNR and SSIM as quantitative indicators of image reconstruction quality. Meanwhile, the performance of the proposed method and other methods on PSNR and SSIM was analyzed for significant difference using various statistical analysis methods, including \(t\)-test, ANOVA, and MANOVA. Table 2 and Figure 4 show the results of the reconstruction of photoacoustic images at different number of sensors. The observed results show that as the number of sensors increased, the artefacts in the reconstructed images were significantly reduced and the image quality was significantly improved. The images directly reconstructed by the TR method generally had problems such as blurring, lack of details, and artifacts, and their average PSNR and SSIM metrics also showed the lowest levels, indicating that their reconstruction results were relatively poor. In contrast, the reconstructed images using the EDSR-M method showed better reconstruction results at all sensor counts, and their average PSNR and SSIM were always at the highest level. The EDSR-M method was able to reduce the artifacts and improve the overall quality of the images, and it could  
recover the detail information in the images, which provided a solid foundation for the best reconstruction results. Figure 5 shows the ground truth image and the PAI reconstructed by each method, and it can be observed that the PAI directly reconstructed by the TR method had obvious blurring and information loss problems, and the overall perception was poor. In addition, the reconstruction effect of double cubic interpolation, as a basic interpolation method, became worse instead, which indicates that it was not suitable for the super-resolution reconstruction of PAIs. Among the various deep-learning methods used, from the perspective of image effect, EDSR-M was able to better recover the fine structure and texture of the image, making the image look clearer and more natural. This demonstrates its superiority in improving image quality and preserving details. From the numerical evaluation point of view, the EDSR-M method also showed superior performance, and it can be observed that the evaluation metrics such as PSNR and SSIM of the EDSR-M reconstructed images achieved optimal values, which are higher than those of other reconstruction methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & \multicolumn{2}{c}{**32 Sensors**} & \multicolumn{1}{c}{**64 Sensors**} & \multicolumn{1}{c}{**128 Sensors**} \\ \hline TR & PSNR (dB) & 29.32 & 29.52 & 30.76 \\  & SSIM & 0.889 & 0.899 & 0.916 \\ \hline EDSR-M & PSNR (dB) & 31.46 & 33.98 & 36.84 \\  & SSIM & 0.903 & 0.930 & 0.960 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average PSNR and SSIM of reconstruction results with different number of sensors. Figure 4: Photoacoustic image reconstruction results for different number of sensors using (**a**) 32 sensors for PAI reconstruction, (**b**) 64 sensors for PAI reconstruction, and (**c**) 128 sensors for PAI reconstruction. Table 3 demonstrates the average numerical results of the reconstruction of each method on the test set. The TR method, as the most primitive direct reconstruction, had a PSNR of only 30.76 dB and an SSIM of 0.916. The bicubic interpolation method performed poorly, which may be due to the fact that its simple interpolation failed to improve the quality of the image efficiently. The image reconstructed by the SRCNN method had a lower SSIM, which was probably due to the fact that the SRCNN failed to recover image details effectively.