## 1 Introduction
### History of Existing Research Work  
Researchers have undertaken these challenges and have tried to deploy various intelligent techniques, such as deep learning approaches. In an early study, [PERSON] et al. [6] developed a carbonate rock porosity detection method that uses the visual inspection of a sample rock as an input. This method [6] relies on the expertise of a scientist for porosity detection. In a ground-breaking work, [PERSON] et al. [7] also developed an image analysis technique that considers colour images (RGB) to detect micro-pores and fractures, although we are unconvinced of its efficacy in the presence of grayscale images. Similarly, the work of [PERSON] et al. [8] describes sample rock pore size distributions by analysing the petrophysical properties derived from a rock's colour images. [PERSON] et al. [9] showcased an image-processing method for porosity detection that uses high-quality image inputs. The pore and fracture sizes are quite large and visually identifiable [9]. In another study of semi-automated rock texture identification image-processing techniques, [PERSON] et al. [10] considered high-resolution images as input samples. We believe that this method might not be able to replicate its performance in the presence of low-quality grayscale images. Additionally, several contemporary studies [11; 12; 13; 14] on pore/fracture detection used simple image analysis techniques. In another study, [PERSON], [PERSON], et al. [15] developed an automated image processing-based method to identify rock fracture segmentation and its trace quantification using a CNN-based model to extract the skeleton of the cracks and a chain code-based method to quantify the fracture traces. They extended this research [16] into rock trace identification using a hybrid of the synthetic minority oversampling technique, random search hyper-parameter optimization and gradient boosting trees. [PERSON], [PERSON], et al. [16] proceeded to propose a novel CNN-based water inflow evaluation method that emulates a typical field engineer's inspection process. This method classifies the undamaged and damaged regions and segments the detailed water inflow damage to the rock tunnel faces. Most of the abovementioned studies used higher-resolution images; however, we suspect that the above proposed methods may not be able to replicate their performances in the presence of lower-quality grayscale images. In the contemporary literature, a few studies employing machine learning, such as the support vector machine (SVM) classifier and deep learning, achieved the pore/fracture detection of various reservoir rocks, and the noteworthy works are illustrated below. [PERSON] et al. [17] used the fractal dimensions of images, gamma rays and resistivity logs as the input dataset to an SVM classifier to detect micro-fractures. However, the detection accuracy was not the best in class, and we suspect that it might suffer from poor image quality with grayscale. [PERSON] et al. [18] employed deep learning and autoencoder techniques to identify the pores/fractures from rock images. Although they used visually discriminating images [18], their detection does not require advanced techniques such as deep learning. This strategy may fail in the presence of a tiny training image sample set with high similarities among images (low divergence). From the above study, it is evident that the existing contemporary detection and classification methods need further advancements to improve their accuracy, particularly in the presence of a tiny training set. A tiny training set is a real problem challenging the accuracy of deep learning strategies for low-quality image samples. Hence, in the current work, we tried to devise a method that works well with a small number of low-quality grayscale image samples.