# HIEN: A Hybrid Interaction Enhanced Network for Horse Iris Super-Resolution
## 3 Experiment and Results
### Ablation Experiments  
#### 3.3.1 Module Ablation  
As presented in Table 4, the results of our ablation study validate the effectiveness of the proposed CQG and ERDB modules. Although the SSIM (0.8552) was marginally lower than that of the '_w/o_ CQG' (SSIM: 0.8553) and '_w/o_ ERDB' (SSIM: 0.8553) variants, the complete model incorporating both CQG and ERDB (_w/_ CQG & ERDB) achieved the best performance in terms of Precision (0.8148), Recall (0.7438), and F1-Score (0.7777). The synergy between CQG and ERDB is evident, as they not only improve iris SR quality  
Figure 8: Visual comparison between HIEN and several classical and state-of-the-art methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Year** & **PSNR** & **SSIM** & **Precision** & **Recall** & **F1-Score** \\ \hline CRAFT & 2023 & 30.2476 & 0.8501 & 0.7386 & 0.6958 & 0.7166 \\ SMFAN & 2024 & 30.4431 & 0.8497 & 0.7371 & 0.6979 & 0.7170 \\ CDFormer & 2024 & 30.4809 & **0.8631** & 0.7804 & 0.7229 & 0.7506 \\ RGT & 2024 & 30.5905 & 0.8551 & 0.7688 & 0.6938 & 0.7294 \\ HIEN & Ours & 30.5988 & 0.8552 & **0.8148** & **0.7438** & **0.7777** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._but also, crucially, preserve discriminative recognition features, which is reflected in the recognition metrics. Notably, we observed that the baseline model (_w/o_ CQG & ERDB) exhibited higher Recall (0.7083) than the '_w/o_ ERDB' variant (Recall: 0.7063) and the '_w/o_ CQG' variant (Recall: 0.7021). However, in terms of F1-Score, the baseline (0.7391) was lower than both the '_w/o_ ERDB' (0.7523) and the '_w/o_ CQG' (0.7479) variants. This nuanced behavior of individual components highlights that while the removal of either module may lead to slight improvements in certain metrics, their joint integration in the full model yields a more balanced and optimal performance overall. We believe this observation reflects a complex interplay in which individual modules, although effective on their own, achieve their full potential primarily through synergy. Figure 9 provides a visual comparison of the different model variants in the module-level ablation study. Specifically, incorporating ERDB alone significantly enhanced the model's capacity for discerning local details, leading to a Precision increase of approximately 2.74 percentage points (pp), although this was accompanied by a slight decrease in Recall of approximately 0.62 pp. This potentially indicates that an excessive focus on local details might compromise the identification of certain samples requiring global context for confirmation. Similarly, the isolated introduction of CQG yielded an even more substantial Precision improvement of approximately 3.21 pp, alongside a smaller decrease in Recall of approximately 0.20 pp. To further substantiate the synergy between the proposed CQG and ERDB modules, we present a qualitative analysis using reconstruction error maps in Figure 10. The visualization reveals that when either CQG or ERDB is employed in isolation, significant residual errors persist in critical iris texture regions. As highlighted by the red boxes, these areas \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Block** & **PSNR** & **SSIM** & **Precision** & **Recall** & **F1-Score** \\ \hline _w/o_ CQG \& ERDB & 30.5450 & 0.8550 & 0.7726 & 0.7083 & 0.7391 \\ _w/o_ CQG & 30.5974 & **0.8553** & 0.8000 & 0.7021 & 0.7479 \\ _w/o_ ERDB & 30.5860 & **0.8553** & 0.8047 & 0.7063 & 0.7523 \\ _w/_ CQG \& ERDB & **30.5988** & 0.8552 & **0.8148** & **0.7438** & **0.7777** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study evaluating the effectiveness of the proposed CQG and ERDB modules. The best and second best results are bold, respectively. Figure 9: Visualization of the ablation results of different modules. exhibit a light blue color, indicating an incomplete restoration of fine-grained patterns. In stark contrast, the complete model, which integrates both modules, demonstrates a marked reduction in error.