# OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method
## 3 Experimental Results and Their Analysis
### Particle Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample No.**} & \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**D**} & \multicolumn{4}{c}{\(\varphi\)} \\ \cline{3-10}  & & **avg** & **max** & **min** & **std** & **avg** & **max** & **min** & **std** \\ \hline \multirow{8}{*}{0003} & HR & 0.1353 & 0.7628 & 0.0025 & 0.0777 & 3.0961 & 8.6712 & 0.3906 & 0.9050 \\  & ESPCN & 0.1343 & 0.7592 & 0.0025 & 0.0798 & 3.1356 & 8.6712 & 0.3974 & 0.9938 \\  & IDN & 0.1318 & 0.7577 & 0.0025 & 0.0780 & 3.1577 & 8.6712 & 0.4004 & 0.9855 \\  & RCAN & 0.1315 & 0.7582 & 0.0017 & 0.0815 & 3.2204 & 9.1712 & 0.3993 & 1.1599 \\  & RDN & 0.1322 & 0.5579 & 0.0017 & 0.0755 & 3.1805 & 9.1712 & 0.8419 & 1.0612 \\  & OmniSR & 0.1298 & 0.7490 & 0.0017 & 0.0778 & 3.2092 & 9.1712 & 0.4170 & 1.0474 \\  & OmniSR-M & 0.1331 & 0.7616 & 0.0025 & 0.0796 & 3.1528 & 8.6712 & 0.3929 & 0.9910 \\ \hline \multirow{8}{*}{0054} & HR & 0.1487 & 5.4074 & 0.0030 & 0.2249 & 2.9676 & 8.3975 & \(-2.4349\) & 0.7508 \\  & ESPCN & 0.1517 & 5.4045 & 0.0042 & 0.2265 & 2.9099 & 7.8975 & \(-2.4342\) & 0.6340 \\  & IDN & 0.1486 & 5.4057 & 0.0048 & 0.2251 & 2.9561 & 7.6899 & \(-2.4345\) & 0.6921 \\  & RCAN & 0.1480 & 5.4062 & 0.0024 & 0.2252 & 2.9747 & 8.6899 & \(-2.4346\) & 0.7495 \\  & RDN & 0.1482 & 5.4062 & 0.0024 & 0.2253 & 2.9763 & 8.6899 & \(-2.4346\) & 0.7608 \\  & OmniSR & 0.1487 & 5.4065 & 0.0042 & 0.2258 & 2.9645 & 7.8975 & \(-2.4347\) & 0.7345 \\  & OmniSR-M & 0.1477 & 5.4065 & 0.0038 & 0.2247 & 2.9746 & 8.0290 & \(-2.4347\) & 0.7342 \\ \hline \multirow{8}{*}{0311} & HR & 0.2125 & 5.3939 & 0.0017 & 0.2450 & 2.4379 & 9.1930 & \(-2.4313\) & 0.7815 \\  & ESPCN & 0.2136 & 5.3932 & 0.0017 & 0.2461 & 2.4269 & 9.1930 & \(-2.4311\) & 0.7744 \\  & IDN & 0.2111 & 5.3934 & 0.0034 & 0.2450 & 2.4474 & 8.1930 & \(-2.4312\) & 0.7563 \\  & RCAN & 0.2121 & 5.3934 & 0.0024 & 0.2440 & 2.4385 & 8.6930 & \(-2.4312\) & 0.7526 \\  & RDN & 0.2107 & 5.3932 & 0.0042 & 0.2443 & 2.4535 & 7.9005 & \(-2.4311\) & 0.7710 \\  & OmniSR & 0.2120 & 5.3932 & 0.0048 & 0.2444 & 2.4383 & 7.6930 & \(-2.4311\) & 0.7473 \\  & OmniSR-M & 0.2117 & 5.3932 & 0.0034 & 0.2448 & 2.4363 & 8.1930 & \(-2.4312\) & 0.7345 \\ \hline \multirow{8}{*}{0617} & HR & 0.2029 & 0.7080 & 0.0017 & 0.0878 & 2.4790 & 9.1930 & 0.4981 & 0.8907 \\  & ESPCN & 0.2080 & 0.8607 & 0.0048 & 0.0894 & 2.3962 & 7.6930 & 0.2164 & 0.6787 \\ \cline{1-1}  & IDN & 0.2035 & 0.8573 & 0.0034 & 0.0900 & 2.4767 & 8.1930 & 0.2221 & 0.8941 \\ \cline{1-1}  & RCAN & 0.2014 & 0.6497 & 0.0042 & 0.0832 & 2.4545 & 7.9005 & 0.6221 & 0.7421 \\ \cline{1-1}  & RDN & 0.2016 & 0.6479 & 0.0066 & 0.0842 & 2.4590 & 7.2396 & 0.6261 & 0.7593 \\ \cline{1-1}  & OmniSR & 0.1993 & 0.6484 & 0.0017 & 0.0827 & 2.4826 & 9.1930 & 0.6250 & 0.8130 \\ \cline{1-1}  & OmniSR-M & 0.2026 & 0.8603 & 0.0034 & 0.0897 & 2.4710 & 8.1930 & 0.2171 & 0.8390 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative results of D (particle size) and \(\varphi\) values for different models on four samples. In summary, it can be fully proven by the comprehensive granularity analysis statistical comparison results that the OmniSR-M model proposed in this paper achieves the best reconstruction results, successfully recovers the high-fidelity information of individual samples in terms of both granularity details and overall distributional features, and demonstrates its advantages in solving image super-resolution problems. Figure 9: Statistical chart of the number of particles. (**a**) Number of particles for different super-resolution methods for 130 samples. (**b**) Error in the number of particles for different methods with HR.