# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
## 4 Experiments
### Comparison to the State of the Art
#### 4.3.3 Parameters and FLOPs  
In Table 3, we compare the number of parameters (Param.), FLOPs, and the average of PSNR and SSIM (Avg. PSNR/SSIM) between our PMFN and other LFSR methods on testing datasets for 4\(\times\) upscaling. Note that our method consumes little computational efficiency but achieves the best results compared with SISR and LFSR methods, especially for the reference view. Furthermore, our PMFN has fewer parameters and better performance than DPT [24]. In summary, our method has a good performance not only on the performance of the model but also on the scores of PSNR and SSIM.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Scale**} & \multicolumn{6}{c}{**Datasets**} \\ \cline{3-7}  & & **EPFL** & **HCInew** & **HCIold** & **INRIA** & **STFgantry** \\ \hline Bicubic & & 29.50/0.935 & 31.69/0.934 & 37.46/0.978 & 31.10/0.956 & 30.82/0.947 \\ VDSR [8] & & 32.64/0.960 & 34.45/0.957 & 40.75/0.987 & 34.56/0.975 & 35.59/0.979 \\ EDSR [19] & & 33.05/0.963 & 34.83/0.959 & 41.00/0.987 & 34.88/0.976 & 36.26/0.982 \\ GB [31] & & 31.22/0.959 & 35.25/0.969 & 40.21/0.988 & 32.76/0.972 & 35.44/0.983 \\ LFSSR [32] & & 32.84/0.969 & 35.58/0.968 & 42.05/0.991 & 34.68/0.980 & 35.86/0.984 \\ resLF [12] & \(2\times\) & 33.46/0.970 & 36.40/0.972 & 43.09/0.993 & 35.25/0.980 & 37.83/0.989 \\ LF-ATO [13] & & 34.05/0.975 & 37.11/0.976 & 44.15/0.994 & 35.96/0.984 & 39.36/0.992 \\ LF-InterNet [14] & & 34.06/0.975 & 37.05/0.976 & 44.37/0.994 & 35.85/0.984 & 38.60/0.991 \\ LF-DFNet [16] & & 34.17/0.976 & 37.31/0.977 & 44.24/0.994 & 36.04/0.984 & 39.28/0.992 \\ MEG-Net [23] & & 33.30/0.971 & 35.98/0.970 & 42.60/0.992 & 35.19/0.981 & 36.53/0.986 \\ DPT [24] & & 33.77/0.975 & 36.93/0.975 & 43.88/0.994 & 35.68/0.984 & 38.66/0.991 \\ Ours & & 34.63/0.977 & 36.92/0.976 & 43.65/0.993 & 36.54/0.985 & 39.33/0.993 \\ \hline Bicubic & & 25.14/0.831 & 27.61/0.851 & 32.42/0.934 & 26.82/0.886 & 25.93/0.843 \\ VDSR [8] & & 27.22/0.876 & 29.24/0.881 & 34.72/0.951 & 29.14/0.920 & 28.40/0.898 \\ EDSR [19] & & 27.86/0.885 & 29.56/0.886 & 35.09/0.953 & 29.69/0.925 & 28.72/0.907 \\ GB [31] & & 26.02/0.863 & 28.92/0.884 & 33.74/0.950 & 27.73/0.909 & 28.11/0.901 \\ LFSSR [32] & & 28.27/0.908 & 30.72/0.913 & 36.70/0.9590 & 30.31/0.945 & 30.15/0.939 \\ resLF [12] & \(4\times\) & 28.14/0.902 & 30.62/0.909 & 36.56/0.968 & 30.22/0.940 & 30.05/0.935 \\ LF-ATO [13] & & 28.74/0.913 & 30.16/0.910 & 37.01/0.970 & 30.88/0.949 & 30.85/0.945 \\ LF-InterNet [14] & & 28.59/0.912 & 30.88/0.914 & 36.95/0.971 & 30.59/0.948 & 30.33/0.940 \\ LF-DFNet [16] & & 28.53/0.910 & 30.66/0.916 & 36.58/0.967 & 30.55/0.944 & 29.87/0.932 \\ MEG-Net [23] & & 28.75/0.916 & 31.10/0.918 & 37.29/0.972 & 30.67/0.949 & 30.77/0.945 \\ DPT [24] & & 28.54/0.911 & 30.92/0.914 & 37.00/0.970 & 30.66/0.948 & 30.65/0.943 \\ Ours & & 29.30/0.917 & 30.93/0.917 & 36.94/0.969 & 31.70/0.950 & 30.97/0.947 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PSNR/SSIM values achieved by different methods for 2\(\times\) and 4\(\times\) SR, the best results are in red and the second best results are in blue.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ang** & **Scale** & **Params.(M)** & **FLOPs(G)** & **Avg. PSNR/SSIM** \\ \hline EDSR [19] & & 38.89 & 40.66 \(\times\) 25 & 30.18/0.911 \\ LF-SSR [13] & & 1.77 & 128.44 & 31.23/0.935 \\ LF-ATO [13] & \(4\times\) & 1.36 & 28.08 \(\times\) 25 & 31.69/0.938 \\ DPT [24] & & 3.78 & 58.64 & 31.56/0.937 \\ PMFN(Ours) [16] & & 3.11 & 49.61 \(\times\) 25 & 31.97/0.940 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparative results in terms of the number of parameters, FLOPSs, and average PSNR/SSIM on testing sets for 4\(\times\) LF image SR methods. FLOPs are calculated on 5 \(\times\) 5 \(\times\) 32 \(\times\) 32 input features.