# OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method
## 4 Conclusions  
In order to solve the conflict between field of view and resolution in rock thin section image acquisition, this paper proposes a lightweight image super-resolution network, OmniSR-M, based on omnidirectional feature aggregation with a multi-branch structure. The Omni self-attention mechanism is used to achieve synchronous interactions of spatial channel features and enhance the capability of contextual modeling. Multi-branching structures allow for the extraction of multi-scale characterizations, thereby enriching the sensory field coverage.  
A large number of experimental results show that OmniSR-M obtains significant improvements in the image quality metrics of PSNR, SSIM, and NIQE, demonstrating excellent super-resolution reconstruction compared with other state-of-the-art models. Compared with the original single-branch OmniSR model, the computational and parametric quantities of OmniSR-M are reduced by 27.66% and 26.56%, respectively, meaning it is more lightweight and efficient; the training process also proves that OmniSR-M converges faster.  
In addition, OmniSR-M demonstrated advanced performance in recovering key information in the rock thin section image, such as the facies porosity rate and statistical features of grain size distribution, and the reconstruction results were highly close to the original image. This proves that OmniSR-M successfully preserves the details and overall  
Figure 10: Particle size error statistical diagram. (**a**) Average particle size of 130 samples with different super-resolution methods. (**b**) Particle size error of different methods with HR.  
feature information of the rock thin section image. In the experiments, we also found that OmniSR-M also had some denoising effect, and in this paper, the large model SAM was introduced for the first time to carry out particle size identification. The application of SAM in the digital rock thin section field is relatively novel, but it shows great potential. The trend in this field is leaning towards developing larger models to improve performance. Additionally work to fine-tune the SAM model for particle size identification is ongoing. In the future, we will continue to explore lightweight model designs for efficient image recovery. We hope that this work will provide valuable insights and inspiration to the rock thin section image super-resolution community.  
All authors contributed to the study conception and design. [PERSON] independently completed the study design and data analysis, while [PERSON] coordinated the sample data and computational resources. [PERSON] took the lead in writing the article, [PERSON] assisted in writing the article. [PERSON], [PERSON], [PERSON] and [PERSON] proofread all drafts, and [PERSON] made the first guidance for the paper and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Nos. 42172163).  
Not applicable.  
The data presented in this study are available on request from the corresponding authors, and the dataset was jointly completed by the team, so the data are not publicly available.  
Author [PERSON] was employed by the company Daqing Oilfield Company Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.