# OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method
## 3 Experimental Results and Their Analysis
### Comparison with the SOTA SR Methodology  
In this paper, we evaluate performance by training several state-of-the-art super-resolution models and quantitatively evaluating them using metrics such as PSNR, SSIM, and the natural image quality evaluator (NIQE). PSNR and SSIM are important criteria commonly used to evaluate image quality and are particularly suitable for the performance evaluation of super-resolution algorithms. Based on the results of the data in Table 2, it is possible to observe the scores of the different models in terms of PSNR and SSIM. The OmniSR and OmniSR-M models performed well in terms of the highest PSNR and SSIM. The PSNR and SSIM of the OmniSR reached 35.7151 and 0.8741, respectively, and those of the OmniSR-M reached 35.7139 and 0.8734, respectively, which demonstrates their excellent ability to preserve image detail and structure. In addition, the PSNR and SSIM metrics of OmniSR-M and OmniSR are very close to each other, indicating that the multi-branch design does not degrade the reconstruction quality.  
Using sample 0003 in Figure 5 as an example, the RCAN, Omni SR, and OmniSR-M algorithms' texture reconstruction is better, which can be seen in the break in the upper left corner; the RCAN, Omni SR, and OmniSR-M algorithms retained the break features, while the other algorithms almost ignored the break features in the upper left corner. From the quantitative metrics, the RCAN, Omni SR, and OmniSR-M achieved PNSR values of 26.52, 26.71, and 26.71, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **OSAG Number** & **Model FLOPs** & **Model Params** & **PSNR** & **SSIM** \\ \hline OmniSR & 1 & 846.998 M & 211.632 K & 35.404 & 0.8716 \\ OmniSR & 2 & 1.423 G & 356.848 K & 35.317 & 0.8727 \\ OmniSR & 3 & 1.998 G & 502.064 K & 34.851 & 0.8712 \\ OmniSR & 4 & 2.574 G & 647.280 K & 35.715 & 0.8741 \\ OmniSR & 5 & 3.150 G & 792.496 K & 33.701 & 0.8492 \\ OmniSR-M & 1 & 1.069 G & 271.600 K & 35.625 & 0.8689 \\ OmniSR-M & 2 & 1.861 G & 475.440 K & 35.714 & 0.8734 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative experimental results of different structural designs of OSAG modules.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **PSNR** & **SSIM** & **NIQE** \\ \hline ESPCN & 33.4577 & 0.8540 & 7.4393 \\ IDN & 34.1920 & 0.8632 & 7.5113 \\ RCAN & 34.5803 & 0.8700 & 7.5288 \\ RDN & 35.0415 & 0.8728 & 7.5041 \\ OmniSR & 35.7151 & 0.8741 & 7.3200 \\ OmniSR-M & 35.7139 & 0.8734 & 7.1823 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results between advanced models.  
NIQE is another image quality assessment metric. Lower NIQE scores usually indicate higher image quality. From the NIQE metric, OmniSR-M achieved the lowest value of 7.1823, indicating that it generates images with the highest visual quality. The OmniSR models obtained a score of 7.3200, indicating that they also perform well in generating high-quality images. Compared to other models, OmniSR and OmniSR-M achieved optimal results on both objective and subjective metrics.  
In summary, it can be concluded that the OmniSR and OmniSR-M models achieved significant performance enhancement compared to the existing models ESPCN, IDN, RCAN, and RDN, which are advanced and effective. The multi-branch structure design enables OmniSR-M to improve the subjective visual quality to a certain extent. Although it fails to surpass Omni SR in objective quantitative indexes, it has achieved similar quantitative results, which indicates that the super-resolution method with the multi-branch structure proposed in this paper has a certain degree of sophistication.