# Highlight Removal Emphasizing Detail Restoration
## 1 Introduction  
In metal images, most pictures exhibit noticeable highlights [1]. Highlights not only cause significant visual disturbance but also, due to the discontinuity between highlight and non-highlight pixel areas, greatly reduce the image's detail information. Among them, welding, as a widely used joining method in industry, may result in an unstable structure if the quality of the weld seam is inadequate. Through weld seam detection, potential risks can be effectively reduced, ensuring the safety of the connected parts. The timely identification and repair of weld seam defects can prevent product failures during use, thereby reducing the costs of later maintenance and replacement. Through effective detection methods, quality issues can be eliminated before product delivery, enhancing production efficiency and reducing costs associated with maintenance and repair. In automated welding processes, timely and accurate detection can quickly eliminate non-conforming products, ensuring the continuity and stability of the production process. Therefore, highlight removal is crucial for the accurate detection of weld seams. Over the past period, researchers have extensively conducted studies on highlight removal [2, 3]. The existing highlight removal algorithms are primarily divided into two categories: those based on multiple images and those based on a single image. Methods based on multiple images include perspective transformation [4, 5] and variations in light source position [6, 7]. On the other hand, single-image-based highlight removal algorithms encompass color segmentation [8], pixel space in the domain [9, 10], color space [11, 12], and repair model based approaches [13] (utilizing deep learning methods). However, most of the above methods overlook the presence of specular pixels and operate under the assumption of perfect diffuse reflection. For instance, these methods have limitations in experimental scenarios with a single background, overly restrictivescene conditions, and generally low ambient light. However, in the real world, most object surfaces exhibit both diffuse and specular reflections. Consequently, when applying these algorithms to process images from real-world scenes, the results are often unsatisfactory [7; 8]. These methods typically cover everyday scenes [14], metal surfaces [15], and special scenarios [16]. Therefore, a method that can be applied to real-world scenes and perform highlight removal in certain special scenarios becomes particularly important. Additionally, the restoration of image details involves recovering texture, edges, and subtle variations, which define the characteristics of the captured scene. This enhancement not only improves the aesthetics of the image but also enriches its interpretability and practicality. The importance of highlight removal and simultaneous enhancement of image details cannot be overlooked. It can reveal hidden information, enrich visual perception, and provide more accurate and meaningful image data for various fields [17]. Therefore, while eliminating highlights from real-world images, restoring and preserving their detail information is particularly important. In the summary by [PERSON] [18] and others on previous approaches, the importance of identifying highlight regions is emphasized. Detecting highlight positions directly influences the final results of highlight removal. Through a systematic analysis of existing highlight removal algorithms, this paper focuses on detecting and removing highlights in individual images while preserving their detail information. The aim is to improve the accuracy of highlight removal and the fidelity of the final results. Therefore, a highlight removal method suitable for various real-world scenarios is proposed. In summary, the main contributions of this paper are as follows:  
This paper introduces a widely applicable highlight removal method that effectively eliminates highlights, enhances image visibility, and improves image details.