## 4 Discussion  
### Reconstruction Accuracy Evaluation  
To quantify the reconstruction accuracy of different networks on the test dataset, four evaluation metrics--RMSE, MAE, PSNR, and SSIM--were used to evaluate the double cubic interpolation (bicubic), SRCNN [14], and TfaSR [13], as well as the five proposed network architectures, and the results are presented in Table 2. A comparison of reconstruction accuracy in two regions led to the following conclusions. In Region 1, Method E achieved RMSE and MAE values of 28.6333 and 17.9121, respectively, which represent significant reductions compared to the other methods. Specifically, its error metrics were about 25% and 34% lower than those of Method A, indicating its superior performance in minimizing reconstruction errors. Furthermore, Method E's PSNR reached 44.9576, surpassing other methods, although its SSIM was slightly lower than that of Method D. In Region 2, Method E again showed the best absolute error metrics, with the RMSE and MAE values being reduced by approximately 32% and 39% compared to Method A. The PSNR also significantly improved to 50.6577, surpassing the other methods. Although the SSIM was slightly lower than that of Method B, it remained high overall, indicating that Method E effectively captures high-frequency information and local details in complex deep-sea terrain. Compared to other methods, Method E achieved reductions in the MAE and RMSE by approximately 5.09-15.71% and 5.22-17.14% in Region 1. The \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Region** & **RMSE (m)** & **MAE (m)** & **PSNR** & **SSIM** &
\begin{tabular}{c} **Uncertainty** \\ **Support?** \\ \end{tabular} \\ \hline Bicubic & 1 & 34.5594 & 21.2502 & 43.3237 & 0.9779 & \(\times\) \\ SRCNN & 1 & 32.1561 & 21.0215 & 43.8784 & 0.9801 & \(\times\) \\ TfaSR & 1 & 28.7836 & 18.8731 & 45.0951 & 0.9837 & \(\times\) \\ A & 1 & 38.4642 & 30.6453 & 42.1614 & 0.9854 & \(\surd\) \\ B & 1 & 34.6503 & 26.4019 & 43.3009 & 0.9853 & \(\surd\) \\ C & 1 & 38.8842 & 29.079 & 42.2996 & 0.9862 & \(\surd\) \\ D & 1 & 38.3193 & 29.9445 & 42.4276 & 0.9902 & \(\surd\) \\ E & 1 & 28.6333 & 17.9121 & 44.9576 & 0.9836 & \(\surd\) \\ Bicubic & 2 & 24.4517 & 14.3723 & 47.1467 & 0.9945 & \(\times\) \\ SRCNN & 2 & 22.2462 & 13.5474 & 48.0248 & 0.9951 & \(\times\) \\ TfaSR & 2 & 17.4057 & 12.3447 & 49.5074 & 0.9969 & \(\times\) \\ A & 2 & 25.0381 & 18.0733 & 21.1249 & 0.9969 & \(\surd\) \\ B & 2 & 18.8816 & 13.7713 & 49.9433 & 0.9759 & \(\surd\) \\ C & 2 & 21.9648 & 15.2127 & 48.4265 & 0.9972 & \(\surd\) \\ D & 2 & 23.5467 & 16.4875 & 47.8225 & 0.9974 & \(\surd\) \\ E & 2 & 16.9891 & 11.0828 & 50.6577 & 0.9969 & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The accuracy indicators of different methods. PSNR and SSIM increased by about 0.31-3.77% and 0.01-0.58%, respectively. In Region 2, the MAE and RMSE decreased by approximately 10.88-22.89% and 2.39-30.5%, he with PSNR and SSIM increasing by 2.23-7.44% and 0.24%. SRCNN and TfafSR produce deterministic outputs with no uncertainty estimates, whereas our methods quantify both aleatoric and epistemic uncertainty. Overall, despite slight differences in perceptual quality metrics, Method E significantly reduced reconstruction errors and improved the PSNR in both regions (see Figure 3), validating its advantage in reconstruction accuracy for complex terrain datasets and providing a solid foundation for further algorithm optimization. To further evaluate the performance of the five network structures in DBM reconstruction tasks, the study compared the five experimental architectures with bicubic interpolation.