# HIEN: A Hybrid Interaction Enhanced Network for Horse Iris Super-Resolution
## 3 Experiment and Results
### Ablation Experiments
#### 3.3.2 Ablation on Number of Modules  
In order to establish the optimal network depth, an evaluation was performed across six distinct configurations, characterized by varying the number of Residual Group (RG) within the set 4, 6 and the number of PATB within the set 1, 2, 3. The results are detailed in Table 5 and visualized in Figure 11.  
**Impact of PATB Module Count. ** When the number of RG was fixed at 4, increasing the PATB count from 1 to 2 resulted in mixed changes: PSNR decreased slightly from 30.5963 dB to 30.5827 dB. However, SSIM saw a marginal increase from 0.8548 to 0.8553, and the recognition-focused metrics improved notably: Precision rose from 0.7402 to 0.8076, Recall from 0.6958 to 0.7208, and F1-Score from 0.7173 to 0.7617. This suggests that two PATB modules enhance recognition performance despite a slight drop in PSNR. Further increasing the PATB count to 3 led to improvements across nearly all metrics compared to PATB = 2: PSNR increased to 30.5988 dB, Precision to 0.8148, Recall to 0.7438, and F1-Score  
Figure 10: Qualitative comparison of different ablation modules. The top row displays the super-resolved results, and the second row shows their corresponding reconstruction error maps. The yellow boxes highlight critical iris texture regions. to 0.7777, while SSIM slightly decreased to 0.8552. This configuration (PATB = 3) also outperformed PATB = 1 across all metrics, indicating that three PATB modules achieve the most effective balance of contextual modeling and detail preservation under RG = 4. Conversely, when RG was set to 6, increasing PATB from 1 to 2 yielded improvements similar to the RG = 4 setting. PSNR decreased slightly (from 30.5871 dB to 30.5806 dB), SSIM increased marginally (from 0.8546 to 0.8547), and all recognition-related metrics improved significantly, with the F1-Score increasing from 0.7341 to 0.7749. This suggests that with deeper residual structures, a moderate increase in PATB count enhances performance. However, further increasing PATB to 3 led to a decline in all metrics compared to PATB = 2: PSNR dropped to 30.5299 dB, SSIM to 0.8531, Precision to 0.7691, Recall to 0.6958, and F1-Score to 0.7306. This implies that three PATB modules may introduce over-parameterization or optimization difficulties when paired with 6 residual groups. **Impact of Residual Group Count. ** For a fixed PATB count of 1, increasing RG from 4 to 6 slightly decreased PSNR (30.5963 dB to 30.5871 dB) and SSIM (0.8548 to 0.8546), while improving Precision (0.7402 to 0.7717), Recall (0.6958 to 0.7000), and F1-Score (0.7173 to 0.7341). With PATB fixed at 2, increasing RG from 4 to 6 also led to a small drop in PSNR (30.5827 to 30.5806) and SSIM (0.8553 to 0.8547), but boosted Precision (0.8076 to 0.8137), Recall (0.7208 to 0.7396), and F1-Score (0.7617 to 0.7749). However, when PATB was fixed at 3, increasing RG from 4 to 6 resulted in a consistent performance drop across all metrics. For instance, comparing RG = 4 and PATB = 3 with RG = 6 and PATB = 3, PSNR declined from 30.5988 dB to 30.5299 dB, SSIM from 0.8552 to 0.8531, Precision from 0.8148 to 0.7691,  
Figure 11: Visualization of the ablation results for different RG and PATB block numbers. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**RG Number** & **PATB Number** & **PSNR** & **SSIM** & **Precision** & **Recall** & **F1-Score** \\ \hline
4 & 1 & 30.5963 & 0.8548 & 0.7402 & 0.6958 & 0.7173 \\
4 & 2 & 30.5827 & 0.8553 & 0.8076 & 0.7208 & 0.7617 \\
4 & 3 & 30.5988 & 0.8552 & 0.8148 & 0.7438 & 0.7777 \\
6 & 1 & 30.5871 & 0.8546 & 0.7717 & 0.7000 & 0.7341 \\
6 & 2 & 30.5806 & 0.8547 & 0.8137 & 0.7396 & 0.7749 \\
6 & 3 & 30.5299 & 0.8531 & 0.7691 & 0.6958 & 0.7306 \\ \hline \end{tabular}
\end{table} 
Table 5: Quantitative analysis of the influence of PATB and ERDB block numbers in the proposed network. Recall from 0.7438 to 0.6958, and F1-Score from 0.7777 to 0.7306. This suggests diminishing returns or even overfitting when both RG and PATB are large.