## 4 Experimental Section
### Comparison with Other Methods  
The WGAN-GP-based color correction model proposed in this work was compared with existing histogram matching and color transfer techniques and was found to produce better results across various performance metrics. Although histogram matching and color transfer techniques demonstrated some effectiveness in color matching, these methods were found to have limitations in reproducing the details of the overall image. In particular, histogram matching showed limitations in maintaining color consistency and resulted in information loss when compared to the  
Figure 6: Precise texture reproduction results. (**a**) Generated image by model at the early stage of training, (**b**) generated image by model at the mid-stage of training, and (**c**) generated image by fully trained model. ground truth, especially in terms of texture and detail. The color transfer technique also exhibited limitations in maintaining the color consistency of the ground truth and was deficient in texture reproduction. On the other hand, the proposed model achieved more natural color matching and texture reproduction as training progressed. In particular, the images generated by the mid-stage model not only showed good overall color matching but also reproduced textures that were fairly similar to the ground truth. At the end of training, the images generated by the model showed a slight decrease in color consistency compared to the mid-stage model. However, better results in texture reproduction were observed, which remained superior to the other methods, as shown in Figure 7.  
This architecture utilizes the Wasserstein distance and Gradient Penalty to enhance training stability and maintain color consistency while preserving the structural features of the image. These mechanisms allow the model to reduce color inconsistencies and enhance the structural integrity of the image, resulting in improved texture reproduction compared to traditional methods. The quantitative analysis uses peak signal-to-noise ratio (PSNR) [27], structural similarity (SSIM) [28], CIE Delta E 2000 (CIEDE 2000) [29], and learned perceptual image patch similarity (LPIPS) [30] as evaluation metrics. PSNR directly reflects the pixel-level mean square error between the generated image and the original image; a higher value indicates less image distortion and a more accurate color distribution. SSIM measures the similarity between images based on brightness, contrast, and structure, reflecting the subjective quality of image restoration. The closer the SSIM value is to one, the more structurally similar the two images are, indicating better quality. CIEDE 2000 measures the color difference between two images, taking into account differences in hue, saturation, and lightness. A lower CIEDE 2000 value indicates less color difference between the two images and greater visual similarity. Compared to PSNR and SSIM, LPIPS is more aligned with  
Figure 7: Comparison with other method’s results. (**a**) Image processed using histogram matching, showing limitations in maintaining color consistency and significant information loss in texture and detail; (**b**) image processed using the color transfer technique, which also shows limitations in maintaining the ground truth’s color consistency and lacks texture reproduction; (**c**) image generated by the early stage of the WGAN-GP-based model, where color distribution is irregular and texture representation is still underdeveloped; (**d**) image generated by the mid-stage model, demonstrating improved color matching and texture reproduction, with textures becoming more similar to the ground truth; and (**e**) image generated by the fully trained WGAN-GP model, showing a slight decrease in color consistency compared to the mid-stage model but offering superior texture reproduction compared to the other methods. human perceptual judgment; a lower LPIPS value indicates a smaller perceptual difference between the generated image and the original image.