# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
## 4 Experiments
### Ablation Study  
We conducted extensive comparative experiments to verify the contributions of different components in our PMFN, including the ResRFB for feature extraction, the FFB for feature fusion and the FEB for reconstruction. As shown in Figure 7, they are visual results with different variants. It is demonstrated that our designed network can achieve the best SR performance. Meanwhile, we investigate the performance of different numbers of DMFM and the influence of the connection mode in our FFB. PMFN-FE w/o ResRFB: The ResRFB is used to extract multi-scale initial features in feature extraction. We investigated the benefit of this block through these two experiments (PMFN-FE + ASPP and PMFN-FE + ResBlock). In order to ensure the parameters of these  
Figure 6: Visual comparisons of different methods on \(\times\)2 and \(\times\)4 reconstruction. Figure 7: Visual results of different variants of our network on “Stanford_Gantry_Lego Knights” for visual 4\(\times\) SR.  
networks are similar, we introduce two blocks (ASPP and ResBlock) to replace the part of ResRFB to extract the features. As shown in Table 4, the value of the average PSNR is decreased by 0.18 and 0.32 for 4\(\times\) SR in EPFL, HCInew, HCloid, INRIA, and STFgantry datasets, respectively. Compared with ResBlock and ASPP, our ResRFB has progressive receptive fields, which are beneficial to extract hierarchical features. Number of DMFMs: This component is the key to our network to achieve feature fusion. Table 5 shows the computational efficiency and quality of the reconstruction affected by different amounts of our DMFMs. It can be observed that the accuracy consistently improves as the number of DMFMs increases. However, the computational efficiency continues to decline and the performance is not significant. It can be observed that the values of PSNR are very similar when the number of DMFMs is between three and five. In this paper, we decided to set three DMFMs to achieve a meaningful trade-off between computational efficiency and the quality of reconstruction. Structure of FFB: We compare the SR performance with different architectures of PFFB. As shown in Figure 8, it demonstrates the different structures based on DMFM. We adopt three DMFMs to investigate the effectiveness of our structure. Compared with the FFB, the structure of the FFB1 is only three cascaded DMFMs, there is no other way to achieve connection. This variant suffers a decrease of 0.48 dB as compared to PMFN. The FFB2 removes the branches of the input feature, which suffers a decrease of 0.16 dB as compared to PMFN. These two demonstrate the effectiveness of dense connection in PMFN. Although the FFB3 and FFB have the same structure, the FFB3 removes the progressive multi-scale convolution. This result suffers a decrease of 0.42 dB as compared to PMFN. Moreover, we remove our FFB in the variant of the PMFN-FF - FFB to investigate this contribution. This result suffers the worst decrease of 0.79 dB in all variants. Our three DFFMs with dense connections can achieve better performance. That is because both low-level and high-level features are beneficial to the SR performance, and progressive multi-scale design can improve feature representation and enlarge the receptive fields.