## 3 Digital Image Acquisition, Processing and Modelling Reconstruction
### Digital Image Processing Analysis  
Digital image processing (DIP) techniques extract important information from images by converting video images into digital form and applying various mathematical procedures. Various fields, such as biology, medicine, and materials science, have applied digital image technology. DIP techniques have enabled the microstructure of aggregates and their engineering properties to be analyzed with proficiency through image recognition and reproduction techniques [89]. DIP techniques involve four steps, namely digitization, enhancement, recovery, and segmentation [90]. The morphological properties of aggregates have been thoroughly analyzed and sufficiently explored to reveal the mechanisms between the complex structural features and the mechanical properties of aggregates. Eventually, the relation between material composition and mechanical properties was established from macroscopic and microscopic perspectives. The DIP method effectively assists in investigating the internal structural properties of aggregated particles. Figure 6 shows a schematic diagram of the process of 2D aggregate identification. Figure 5: The laser scanner. Figure 6: Schematic diagram of 2D aggregate particle identification. [PERSON] et al. [91] proposed a simplified DIP based on the concept of volume factor to measure the particle shape index of fine aggregates. Scanning the aggregate image by distinguishing different resolution values, the volumetric coefficient of the aggregate was varied while the scan resolution was varied, and the value of the volumetric coefficient was stabilized at 300 dpi. Subsequently, a size correction factor \"C\" was proposed by comparing the manual measurement with the DIP technique. The results show that the actual largest size is equal to the multiplication of the DIP largest size and \"C\". The image processing technique further validates the accuracy of the test. However, there is an uncertainty in the correction factor for the volumetric coefficients presented in this study, which is limited by the particle shape. [PERSON] et al. [92] used image technology to characterize shape indices such as elongation (ER), flatness ratio (FR), flatness and elongation coefficient (FEC), sphericity coefficient (SC), angle index (AI) and equivalent elliptical perimeter ratio (EEPR), and further investigated the effect of coarse aggregate morphological characteristics on the performance of heavy hot mix asphalt (HMA) pavements. After vibratory compaction of the asphalt mixture, there is an uneven distribution of aggregates on the surface, which may lead to pavement segregation. The motion and rutting mechanism of coarse aggregates under load were analyzed in depth by [PERSON] [70]. The movement characteristics of coarse aggregates in different asphalt mixtures and the correlation between the movement fine-scale parameters and the rutting depth were analyzed from the fine-scale perspective. The mechanical properties and service life of asphalt pavement, etc., are directly influenced by the degree of homogeneity of the asphalt mixture. [PERSON] et al. [93] proposed an image classification separation detection method for asphalt mixtures during construction, which used image processing methods to evaluate pavement segregation and effectively predict the degree of segregation during the paving process. The texture features in IPM are obtained by the DIP technique and combined with the Naive Byers classification algorithm to classify the isolation state of images of compacted asphalt pavement (ICP). It provides a promising solution for the segregation evaluation of asphalt pavement construction and management. The overall accuracy of the proposed method is 87.50%, which is a significant improvement over manual detection. Accurate characterization of the aggregate morphology is significant for the initial design phase of the pavement. [PERSON] et al. [55] studied 9.5-13.2 grades of limestone and basalt by dividing different aggregate prism angles.