## 6 Experimental Results  
As shown in Figure 11, more detailed information can be observed after image enhancement using the GTIE-IACE algorithm. In contrast, although HE and CLAHE [21] can equalize the histogram of the image, these two algorithms are not effective in processing the microstructural changes during melting and solidification of BiFeO\({}_{3}\). Oscillations and artifacts appear during wavelet decomposition and reconstruction. The wavelet transform is an operation based on local windows. When the window intersects with the image boundary, this results in incomplete data at the boundary, thus introducing oscillations and artifacts. EnlightenGAN is an efficient unsupervised generative adversarial network that provides satisfactory low-light image enhancement by using deep learning and generative adversarial network techniques [25]. However, EnlightenGAN is not effective in processing images of melting and solidification of high-temperature materials. Although the overall brightness of the image is improved, there is no significant change in the contrast and the detail information is not clear. The GTIE-IACE algorithm proposed shows a better performance. The algorithm is able to more effectively enhance the details of the melting and solidification processes while improving the brightness and contrast of the image. This means that the GTIE algorithm has an advantage in extracting and enhancing the key details and hence is a better choice for processing this particular type of image. According to the histograms, the grey level values of the image enhanced using the GTIE algorithm effectively cover the entire grey level range. This indicates the best enhancement performance among different algorithms (Figure 12). A gradient histogram is a statistical graph that describes the distribution of gradients of an image. In image processing, the gradient indicates the magnitude of pixel intensity changes in an image. The shape of the gradient histogram reflects the edge features and texture information of the image, and it helps to understand the structure and content of the image. Analyzing gradient histograms is a common means in image processing and computer vision tasks. The gradient histograms of enhanced high-temperature material images using different contrast enhancement algorithms are shown in Figure 13. The gradient histograms of the image processed using GTIE-IACE have a more uniform distribution, highlighting image details at different gradient levels. Figure 11: Processing of BiFeO\({}_{3}\) solid-liquid interface images using different contrast enhancement algorithms. CEIQ, EME, and MOE are used to evaluate the effectiveness of the image processing algorithms in contrast enhancement. A higher score indicates that the algorithm gives a better contrast enhancement effect. The NR-IQA for each image in Figure 11 is shown in Table 1. Evaluation scores of different image enhancement algorithms, according to which, the GTIE-IACE algorithm performs the best in terms of image contrast enhancement. After processing using the proposed solid-liquid interface image contrast enhancement algorithm, the image sequence of the BiFeO\({}_{3}\) melting and solidification processes is shown in Figure 14.  
Figure 12: Gray level histograms of BiFeO\({}_{3}\) solid-liquid interface image processed by different contrast enhancement algorithms. Figure 13: Gradient histograms of BiFeO\({}_{3}\) solid-liquid interface images processed by different contrast enhancement algorithms. Crystal growth kinetics describes the relationship between the velocity of solid-liquid interface movement and the driving force, where the driving force is usually expressed as undercooling, which can be obtained through temperature gradients. In order to accurately study crystal dynamics, a clear solid-liquid interface is a necessary condition. If the solid-liquid interface is not clear, it will affect the accuracy of the kinetic function.