# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 4 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & \(\mathbf{Q^{AB/F}}\uparrow\) & \(\mathbf{Q_{MI}}\uparrow\) & \(\mathbf{Q^{P}}\uparrow\) & \(\mathbf{Q_{W}}\uparrow\) & \(\mathbf{Q_{E}}\uparrow\) & \(\mathbf{Q_{CB}}\uparrow\) \\ \hline \multicolumn{8}{c}{Methods based on image transform domain} \\ \hline DWT [7] & 0.6850 & 0.8677 & 0.2878 & 0.8977 & 0.8356 & 0.6117 \\ DTCWT [54] & 0.6929 & 0.8992 & 0.2925 & 0.8987 & 0.8408 & 0.6234 \\ NSCT [8] & 0.6901 & 0.9039 & 0.2928 & 0.9030 & 0.8413 & 0.6174 \\ CVT [55] & 0.7243 & 0.8968 & 0.7966 & 0.9388 & 0.9023 & 0.7277 \\ DCT [7] & 0.7031 & 0.9383 & 0.7825 & 0.9093 & 0.8073 & 0.6624 \\ GFF [56] & 0.6998 & 1.0020 & 0.2952 & 0.8982 & 0.8351 & 0.6518 \\ SR [57] & 0.6944 & 1.0003 & 0.2921 & 0.8984 & 0.8309 & 0.6406 \\ ASR [58] & 0.6951 & 1.0024 & 0.2926 & 0.8986 & 0.8308 & 0.6413 \\ MWGF [59] & 0.7037 & 1.0545 & 0.3176 & 0.8913 & 0.8107 & 0.6758 \\ ICA [60] & 0.6766 & 0.8687 & 0.2964 & 0.9084 & 0.8219 & 0.5956 \\ NSCT-SR [61] & 0.6995 & 1.0189 & 0.2949 & 0.9000 & 0.8385 & 0.6501 \\ \hline \multicolumn{8}{c}{Methods based on image spatial domain} \\ \hline SSSDI [62] & 0.6966 & 1.0351 & 0.2915 & 0.8961 & 0.8279 & 0.6558 \\ QUADTREE [63] & 0.7027 & 1.0630 & 0.2940 & 0.8962 & 0.8265 & 0.6681 \\ DSIFT [61] & 0.7046 & 1.0642 & 0.2954 & 0.8977 & 0.8354 & 0.6675 \\ SRCF [46] & 0.7036 & 1.0590 & 0.2954 & 0.8978 & 0.8369 & 0.6669 \\ GFDF [64] & 0.7049 & 1.0524 & 0.2974 & 0.8989 & 0.8399 & 0.6657 \\ BRW [65] & 0.7040 & 1.0516 & 0.2964 & 0.8984 & 0.8371 & 0.6650 \\ MISF [66] & 0.6984 & 1.0391 & 0.2945 & 0.8929 & 0.8063 & 0.6607 \\ MDLSR\_RFM [20] & 0.7518 & 1.1233 & 0.8294 & 0.9394 & 0.9021 & 0.8064 \\ \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison of different MFF methods on the Lytro dataset [46]. Figure 8: Comparison of decision maps generated by different methods on the MFI-WHU dataset [36]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Model Size (M)**} & \multicolumn{2}{c}{**FLOPs (G)**} & \multicolumn{2}{c}{**Time (s)**} \\ \hline \multicolumn{6}{c}{End-to-end methods based on deep learning} \\ \hline IFCNN-MAX [24] & 0.08 & 8.54 & 0.09 \\ U2 Fusion [25] & 0.66 & 86.40 & 0.16 \\ SDNet [35] & 0.07 & 8.81 & 0.10 \\ MFF-GAN [36] & 0.05 & 3.08 & 0.06 \\ SwinFusion [27] & 0.93 & 63.73 & 1.79 \\ MUFusion [37] & 2.16 & 24.07 & 0.72 \\ FusionDiff [29] & 26.90 & 58.13 & 81.47 \\ SwinMFF [26] & 41.25 & 22.38 & 0.46 \\ DDBFusion [28] & 10.92 & 184.93 & 1.69 \\ \hline \multicolumn{6}{c}{Decision map-based methods using deep learning} \\ \hline CNN [22] & 8.76 & 142.23 & 0.06 \\ ECNN [23] & 1.59 & 14.93 & 125.53 \\ DRPL [19] & 1.07 & 140.49 & 0.22 \\ SESF [30] & 0.07 & 4.90 & 0.26 \\ MFIF-GAN [31] & 3.82 & 693.03 & 0.32 \\ MSFIN [10] & 4.59 & 26.76 & 1.10 \\ GACN [67] & 0.07 & 10.89 & 0.16 \\ ZMFF [11] & 6.33 & 464.53 & 165.38 \\ LightMFF & **0.02** & **0.06** & **0.02** \\ Reduction (\%) & 60.00\% & 98.05\% & 66.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of computational efficiency across different learning-based MFF methods.