# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 4 Experiments
### Experimental Results  
**Qualitative comparison. ** The decision maps offer more direct insights into a network's performance than the fused images themselves. For qualitative comparisons of decision maps, we exclusively present the initial decision maps generated by each network, which helps us avoid variations that might come from different post-processing techniques. We have visualized these decision maps, which are responsible for generating the fusion results, in Figure 5. Visual inspection suggests that LightMFF produces the most visually compelling decision maps among all the approaches we compared. Compared to the Lytro dataset [46], the MFFW dataset [53] offers more challenging scenarios due to its more pronounced defocus dispersion effects. To further validate our approach under these demanding conditions, we conducted additional visual comparisons on this dataset, as shown in Figure 6. The comparison reveals that most methods, with the exceptions of our proposed LightMFF and MFIF-GAN [31], show noticeably degraded performance on this dataset when compared to their results on the Lytro dataset. Several approaches struggle with large-area misclassification, which severely impacts the quality of their fusion results. In contrast, LightMFF maintains exceptional performance, consistently producing the most accurate decision maps among all compared methods. In Figure 7, we compared the fusion results of different methods using the \"coffee cup\" example from the MFFW dataset. As the figure illustrates, particularly in the boundary regions of the coffee cup, only ECNN [23] and our proposed LightMFF accurately differentiate between focused and defocused areas along the cup edges. Both ECNN and LightMFF achieve clean fusion results without notable artifacts. In contrast, other approaches exhibit visible fusion artifacts in these challenging regions. This robust performance demonstrates LightMFF's superior capability in handling scenes with strong defocus effects. Figure 5: Comparison of decision maps generated by different methods on the Lytro dataset [46]. Figure 6: Comparison of decision maps generated by different methods on the MFFW dataset [53]. Figure 7: Fusion results of various SOTA methods on the “Coffee cup” example from the MFFW dataset [53]. Additionally, we conducted qualitative comparisons on representative image pairs from the MFI-WHU dataset [36]. The corresponding decision maps are shown in Figure 8. The selected cases represent three challenging scenarios: small object detection (first row), high-contrast scenes (second row), and large homogeneous regions (third and fourth rows). The proposed LightMFF consistently outperforms all competing methods across all test cases, demonstrating superior accuracy in every scenario. **Quantitative comparison. ** We conduct comprehensive quantitative evaluations on the widely-used Lytro dataset [46] to assess our method's performance. Table 1 thoroughly compares our approach against 36 other methods, highlighting the best-performing methods in bold and the second-best in underline. For each metric, an upward arrow indicates that a higher value is better. Methods highlighted with a background color represent our proposed approach or the complete proposed model.As shown in Table 1, the proposed LightMFF achieves state-of-the-art performance across most metrics. Specifically, LightMFF obtains the highest scores in five out of six metrics: \(Q^{AB/F}\) (0.7588), \(Q_{MI}\) (1.1462), \(Q^{P}\) (0.8450), \(Q_{E}\) (0.9061), and \(Q_{CB}\) (0.8067). For the remaining metric \(Q_{W}\), our method achieves the second-best score (0.9400), only marginally lower than GACN (0.9405) [67] by 0.0005.