## 1 Introduction
Their inherent robustness against fluctuations in ambient light, shadows, and specular reflections is a testament to their superiority over conventional 2D methods. Moreover, the provision of multiple vantage points ensures an exhaustive coverage that is essential for identifying defects which may remain concealed or ambiguous in a single 2D image. This all-encompassing approach significantly amplifies the overall effectiveness of defect detection, underscoring the superior capabilities of 3D visual measurement in surface defect analysis. [PERSON] [18] introduced a multi-exposure structured light method for the three-dimensional reconstruction of battery surfaces. By utilizing height-gray transformation, the anomalous parts of the three-dimensional point cloud are converted into two-dimensional images, effectively addressing the challenge of classifying surface defects with two-dimensional detection. [PERSON] [19] utilized a depth camera to capture color images and depth maps of cabbages for point cloud reconstruction. Subsequently, the normal vector for each point in the point cloud was estimated using a least-squares plane fitting method, and the curvature feature of the point cloud was calculated. More specifically, the curvature estimation was performed by fitting a quadratic surface to the local region around each point. [PERSON] [20] employed a photometric stereo vision and established a device with four light sources to acquire multi-angle images of road surfaces, subsequently introducing optimal angles for different light sources to restore the three-dimensional morphology of road surface texture for defect detection. [PERSON] [21] introduced a new method which is based on photometric stereo vision with four light sources to recover the lost depth information in NPP images and improve the detection of surface defects on textured metal parts. While machine vision has, to a significant extent, resolved the majority of defect detection challenges in manufacturing, thereby substantially boosting industrial productivity, it encounters notable hurdles when dealing with textured materials. Materials such as wood, leather, textiles, and specialty papers present unique difficulties due to the impact of lighting angles and subtle surface irregularities at the microscopic level. These factors can mask the distinct features of scratch defects, thereby significantly escalating the intricacy involved in their detection. This phenomenon is vividly illustrated in Figure 1.  
To address the challenges posed by textured materials, we developed an enhanced scratch defect detection method for textured materials based on optimized photometric stereo vision and FFT-Gabor filtering. Initially, we developed an Enhanced Scratch Detection method for textured materials. This method is characterized as a composite-enhanced defect detection method that combines optimized photometric stereo vision hardware with an FFT-based image enhancement algorithm. The main contributions of this paper can be summarized as follows:  
1. Optimized Hemispherical Photometric Stereo Vision Device: We designed and optimized a new type of hemispherical photometric stereo vision image acquisition device with selective light source angles. Based on the selectivity of the multi-angle light source, we can choose the light source angle by ourselves according to the irregularity and complexity of the texture of the material, so as to achieve a balance between the acquisition time and the quality of the input image. 2. Photometric Stereo Vision Algorithm with Eight Light Sources: Using images captured under eight distinct light source angles, we implemented a photometric stereo vision algorithm. This approach mitigates issues caused by specular reflections or shadows under single-light-source conditions, enabling the complete capture of both texture and defect information via Albedo and Curl Gradient images. 3.