# OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method
###### Abstract  
With the rapid development of digital core technology, the acquisition of high-resolution rock thin section images has become crucial. Due to the limitation of optical principles, thin section imaging involves a contradiction between resolution and field of view. In order to solve this problem, this paper proposes a lightweight, fully aggregated network with multi-branch structure for super resolution of rock thin section images. The experimental results on the rock thin section dataset demonstrate that the improved method, called OmniSR-M, achieves significant enhancement compared to the original OmniSR method and also surpasses other state-of-the-art methods. OmniSR-M effectively recovers image details while maintaining its lightweight nature. Specifically, OmniSR-M reduces the number of parameters by 26.56% and the computation by 27.66% compared to OmniSR. Moreover, this paper quantitatively analyzes both the facies porosity rate and grain size features in the application scenario. The results show that the images generated by OmniSR-M successfully recover key information about the rock thin section.  
deep learning; rock thin section image; lightweight neural networks; super-resolution reconstruction +
Footnote †: journal:  
methods often results in the generated images looking too smooth and lacking details, which predisposes them to being less capable of reconstructing texture in high-frequency images such as thin slices. In order to solve this problem, some studies have proposed super-resolution methods based on perceptual loss [11; 12; 13; 14; 15; 16], but since this class of methods focuses more on improving the quality of human-perceived images, it is more applicable to natural images.  
The attention mechanism is better able to capture the details of the image [17], its structure, and its texture information, focusing more on pixel-level reconstruction. In order to achieve aggregation in both spatial and channel dimensions, omnidirectional self-attention is proposed for fully aggregated networks, which realizes the full fusion of multi-scale features, including local, mid-range, and global scales [18]. Therefore, this paper proposes a lightweight super-resolution method for fully aggregated networks with multi-branch structure, comparing several state-of-the-art super-resolution methods [16; 18; 19; 20; 21], and quantitatively evaluates them in terms of structural similarity, peak signal-to-noise ratio, and perceptual quality. In addition, this paper starts from the application of thin section images and proposes the use of the large model Segment Anything (SAM) for semantic segmentation to compute the granularity information and compare it with high-definition images in order to assess the quality of the model's restoration at the edges of the grains [22]. The traditional threshold segmentation method for extracting the facies porosity rate was also used as a way of comparing the effectiveness of different algorithms.  
In this work, the main contributions can be summarized as follows:  
1. We propose a full aggregation network structure with a multi-branch structure, including a spatial attention mechanism and channel attention mechanism, which can learn the features of different scales better than the inherent full aggregation network structure.
2. The Omni-M method achieves a lighter weight in terms of Model FLOPs and Model Params by 27.66% and 26.56%, respectively, compared to the intrinsic method.
3. For the first time, the use of the conducted large-model SAM method is proposed for application to particle size analysis of rock thin sections, and it is proposed that the super-resolution results be evaluated through two application scenarios, namely, facies porosity rate and grain size.