## 3 Results and Discussion
### Color Reproduction  
In the color gamut exploration experiment, a genetic algorithm was used to find the color gamut range that laser-induced coloring could achieve in stainless steel, laying the foundation for subsequent color reproduction work. In color reproduction experiments, since the colors of common images are based on ink and electronic ink color matching, the color gamut is much larger than the color gamut that stainless steel materials can form through laser induction. Therefore, it is necessary to map the colors of the image to the color gamut of stainless steel materials to complete the color reproduction work. For this purpose, we have developed an image color mapping toolkit and an image vectorization toolkit, and we have used the MATLAB language to complete the programming work for both toolkits. The image color mapping toolkit is based on color difference calculation, color separation, and index colors to achieve the matching of colors between the target image and the color gamut. The input is an image, and the image is first color-separated. Then, the toolkit calculates the color difference between each separated color and the colors in the for laser coloring devices to perform laser coloring. The vectorized image information exists in the format of vector points, which is convenient for control and laser processing. In this paper, a cartoon tiger color gamut to match the laser parameters corresponding to the most suitable color in the stainless steel color gamut. The output is the laser parameters corresponding to the color mapped by the color gamut. The image vectorization tool converts pixel information in the image into vector-format information through edge detection and curve fitting, which is necessary image and a cartoon temple image were prepared. First, the color of the target image was mapped onto the gamut of colors achievable on stainless steel by using the image color mapping toolkit. Then, the color-gamut-mapped image was divided through color separation into four basic color laser processing images that are within the color gamut of stainless steel, such that they can be presented through laser coloring. Finally, these four basic color images were converted to vector format for laser processing using the image vectorization toolkit; the results are shown in Figure 6a-d. Figure 6a,c are the images after color gamut mapping of the original images, and Figure 6b,d are the color reproduction images of the laser-induced coloring; the resolutions of the tiger image and temple image are 166 dpi \(\times\) 154 dpi and 209 dpi \(\times\) 211 dpi, respectively. As shown in the figure, the color hue and saturation of the tiger's face, tail, and front leg parts in Figure 6b remained basically the same as those of the corresponding positions in Figure 6a, which indicates that our mapping algorithm was effective in mapping the color hue of the original image to the color gamut that the stainless steel surface is able to achieve. Similarly, in the temple image, most of the color tones reproduced in Figure 6d match the mapped image in Figure 6c, and the color tones  
Figure 5: Schematic diagram of the genetic algorithm convergence verification experiment. (**a–e**) represent the maximum predicted color gamut values for generations 16 to 20, respectively. Among them, the 17 th generation reached a convergence value of 0.0022. (**c–e**) correspond to the convergent iterations that verify the experimental results. were basically the same between the color-gamut-mapped image and the reproduced color image, which is the purpose of our color gamut mapping. However, the color tone of the tiger's hind legs and other parts in Figure 6b was distorted, resulting in serious color cast issues, which we did not want to see. We successfully conducted color reproduction experiments on a tiger image and a temple image. However, a significant color cast issue arose during the experiments and was particularly noticeable in the tiger images.