# Highlight Removal Emphasizing Detail Restoration
## 4 Results
### Qualitative Evaluation  
To conduct a qualitative assessment, this paper categorized the collected highlight images into four major classes: metal, plastic, glass, and decorative items. These images were sourced from the SHIQ dataset and a dataset of real-world weld seam images collected \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Abbreviation** \\ \hline Separation of reflection components by sparse non-negative matrix factorization [20] & [PERSON] [20] \\ \hline Specular highlight removal for real-world images [31] & [PERSON] [31] \\ \hline Simple and efficient method for specularity removal in an image [10] & [PERSON] [10] \\ \hline Real-time highlight removal using intensity ratio [14] & [PERSON] [14] \\ \hline General improvement method of specular component separation using high-emphas filter and similarity function [3] & [PERSON] [3] \\ \hline Deep multi-class adversarial specularity removal [32] & [PERSON] [32] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: These methods are replaced with abbreviations later in this article. Figure 5: Comparison of highlight removal effects on the metal surface: (**a**) input image, (**b**) [PERSON] [20], (**c**) [PERSON] [31], (**d**) [PERSON] [10], (**e**) [PERSON] [14], (**f**) [PERSON] [3], (**g**) [PERSON] [32], (**h**) our method. Figure 6: Comparison of highlight removal effects on the plastic surface: (**a**) input image, (**b**) [PERSON] [20], (**e**) [PERSON] [31], (**d**) [PERSON] [10], (**e**) [PERSON] [14], (**f**) [PERSON] [3], (**g**) [PERSON] [32], (**h**) our method. in this paper. After contrasting the images in each category, three reference metrics were employed to evaluate the quality of the highlight removal images. They were the MSE (mean squared error), measuring the average squared difference between two images at each pixel; PSNR (peak signal-to-noise ratio), assessing the ratio of signal to noise; and SSIM (structure similarity index measure), comparing the brightness, contrast, and structure of the images. In conclusion, these metrics helped to determine the effectiveness of the highlight removal methods. Figure 5 illustrates the comparison of the highlight removal effects between this paper's approach and others on the surface of metal. In Figure 5b, the highlights are not effectively removed, and many artifacts are present. In Figure 5d,\(f\),fig, the image quality significantly degrades after highlight removal. In Figure 5e, although the highlights were removed, there are noticeable artifacts at the transitions in the highlight region, with severe loss of details. Figure 5h represents a deep learning method, showing noticeable color discrepancies in the restoration. While this method effectively removed highlights, it also exhibited certain artifacts. Table 2 presents the mean performance metrics of the different methods on these three images. The optimal result is marked in bold. Figure 6 shows the results of highlight removal on a plastic surface. It can be observed that on the plastic surface, most methods resulted in some degree of color deviation after processing. This was mainly because the existing methods for highlight removal failed to constrain the highlight area, thereby mistakenly identifying the white background as highlights. Additionally, some methods exhibited a pseudo-shadow in the original highlight position after removing the highlights, indicating an incomplete elimination of highlights. These two issues are clearly visible in Figure 6e. The approach proposed in this paper performed better in suppressing pseudo-shadows and reducing color deviations in the highlight areas compared to the existing methods. Table 3 presents the average performance metrics for the images in Figure 6. The optimal result is marked in bold. The results of highlight removal on glass surfaces shown in Figure 7 were compared. The existing methods achieved relatively balanced highlight removal on glass surfaces, but color deviation was observed to some extent, indicating an area for potential optimization in our future work.