# OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method
## 3 Experimental Results and Their Analysis
### Pore Space Segmentation Analysis  
To focus on a specific application scenario involving thin section images, this paper extracts the facies porosity rate of the thin section image using threshold segmentation to evaluate the reconstruction effects of different methods, Threshold segmentation is a basic image segmentation method, and its main idea is to optimize the threshold value by using  
Figure 5: Individual model reconstruction results. As shown in figure, taking sample 0003 as an example, (**a**) is the original HR image of the rock thin section; (**b**) is the cropped 200 \(\times\) 200 area image from the original HD image, and the red box is the sampling area; (**c**) is the LR image after downsampling by four times; (**d**) is the super-resolution result map of the ESPCN model; (**e**) is the super-resolution result map obtained by the IDN model; (**f**) is the super-resolution result map obtained by the RCAN model; (**g**) is the super-resolution result map obtained by RDN model; (**h**) is the super-resolution result map obtained by OmniSR model; and (**i**) is the super-resolution result map obtained by OmniSR-M model. a grayscale histogram and then divide the image into different regions so that the pixels within each region have similar attributes or features [28]. In applications of thin section images for finding the facies porosity rate, threshold segmentation is used to separate the pore portion of the image from the rest of the region in order to quantify the proportion of the pore portion within the whole image. Since only a part of the region of the whole thin section image was cropped for the calculation in the experiment, it leads to a large facies porosity rate, but this does not affect the absolute error of comparing the facies porosity rate of the reconstructed images from multiple models with the HR. In terms of the evaluation of the facies porosity rate, the rock thin section data of four samples were randomly selected in the experiment for the comparison of facies porosity rate calculation, which was 0003, 0054, 0311, and 0617, and the detailed calculation results are shown in Table 3. The HR is used as the baseline to extract the facies porosity rate, which is then compared with the porosity of the reconstruction results of each advanced super-resolution method, and the relative error is calculated to quantify the different super-resolution methods. Taking sample 0054 as an example, as shown in Figure 6, the HR facies porosity rate is 24.68%, and from the LR results, it can be seen that the downsampling caused an error in the image porosity occupancy. In contrast, Omni SR and OmniSR-M have the smallest relative errors in the facies porosity rate (24.78% and 24.72%, respectively) compared with the facies porosity rate of the original HR image. The pore occupancy results for OmniSR and OmniSR-M are closest overall to the value of HR. As in sample 0617, the pore occupancy error for the OmniSR series of models is very small and accurately recovers the value of HR. The reconstructed pore occupancy ratios of other models ESPCN, IDN, RCAN, etc. are not accurate enough and differ somewhat from the HR values. This once again validates that the OmniSR family of models can recover high-quality pore features in rock thin section image samples. The effect of multi-branch OmniSR-M was close to that of single-branch OmniSR, maintaining the accurate recovery of the pores. Test results on four samples demonstrate that the OmniSR series of models can achieve excellent performance in super-resolution applications of rock thin section pore images. Figure 6: \(Cont.\)