# Mineral Identification Based on Deep Learning Using Image Luminance Equalization
## 2 The Proposed Method
### A New Algorithm Based on HELaplace
In contrast to the dual-energy CT chemometric calibration method [34], our work does not require the use of instruments for the medical X-ray tomography of minerals. Compared to the method using polarized light microscopy to obtain images [10], which can only identify five minerals, our method can identify 50 species with similar accuracy. Similarly, compared to the work of [PERSON] et al. [11] which could only \begin{table}
\begin{tabular}{c c} \hline
**Method** & **Accuracy** \\ \hline YOLOv5 & 85.31\% \\ HE + YOLOv5 & 87.14\% \\ Laplace + YOLOv5 & 86.82\% \\ HELaplace + YOLOv5 & 95.63\% \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of the accuracy of different methods. Figure 6: Accuracy comparison of specific mineral species. distinguish between resin and quartz, we were able to differentiate more minerals and maintain a similar accuracy. Furthermore, in contrast, we do not need special instruments to obtain the picture data under the polarized light microscope, we only need to take pictures of the minerals to perform the identification. In contrast to the work of [PERSON] et al. [16] who used Mohs hardness and images to identify minerals, our work does not require the use of instruments to obtain Mohs hardness. It is worth noting that the aforementioned work on identification using mineral images was experimentally measured using images taken under normal lighting, and when experiments were conducted using images taken under excessively dark or excessively bright conditions, the accuracy rate would be reduced to varying degrees. ### Objective Evaluation Indicators

Since it is difficult to obtain the normal illumination image corresponding to the image under abnormal illumination, for the image quality after enhancement, natural image quality evaluator (NIQE) [36] was used in this paper. NIQE is a non-reference image quality index often used to measure the quality of the image, a smaller NIQE indicating a better the quality of the measured picture. In addition, we used the lightness-order-error (LOE) [37] to evaluate the contrast of the enhanced image with the original illuminated image. LOE reflects the natural retention of the image, and a smaller value indicates that the image has a better order of luminance and therefore looks more natural. Table 6 shows the objective evaluation data of the corresponding methods in Figure 1. From the data in the table, we can see that the LOE of our algorithm is lower than that of the Laplace algorithm, and it is the lowest among all algorithms, indicating that we have the best result in maintaining the naturalness of the image. Furthermore, the NIQE value of the algorithm in this paper is the lowest among all algorithms, which indicates that the method in this paper does not produce much detail, thus blurring and color distortion to the original image. ## 5 Conclusions and Future Work

In this paper, we propose a deep learning mineral identification method based on luminance equalization. Compared with traditional mineral identification methods, we reduce the reliance on the researcher's experience and instruments. Compared with traditional mineral identification algorithms, we reduce the influence of illumination intensity on mineral identification and greatly improve the accuracy rate. In the deep learning recog \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **HE** & **Laplace** & **HELaplace** \\ \hline LOE & 222.6444 & 156.2836 & 150.7435 \\ NIQE & 25.3780 & 41.7903 & 25.2050 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Result of LOE and NIQE.