## 3 Image Processing Technique Used
### Image Enhancements  
Identifying aggregates in an asphalt mixture can be simple for objects that have uniform brightness that can make objects well separated. Frequently, an image of asphalt mixture has a low signal/noise ratio due to the presence of constituent of minerals in the aggregate. This also results in the brightness values of the image not making full use of the object-separation. For those difficulties, the image enhancement techniques can make it possible to use the image for manipulation. If the target aggregate objects are particularly dark, small, and irregular in shape, or the brightness histogram of the image appears poor and single peak, a specific threshold value cannot distinguish the image into the background and aggregate objects. In order to correct this difficulty, the image is intended to extend the brightness histogram to improve the contrast, which is referred to as brightness manipulation. There are two categories on the image enhancement techniques, which are linear and non-linear methods, to adjust the color or gray-scale range of an image filling the computer display's dynamic range and improving the contrast in the image. The terms linear and non-linear refer to the function that is applied to the data to perform image enhancement. Figure 4 shows the functions to increase the brightness to varying degrees over different ranges of the data. The transformation of the gray scale is presented as follows. When comparing the image texture of two or more images, it is common to  
Figure 3: Compaction energy index (CEI). first normalize their histograms to a standard histogram. Equalization is the most popular non-linear histogram normalization technique that attempts to change the histogram. If some gray-scale values are underutilized, it might be better to selectively spread out the displayed gray levels in the pick area, compressing them in the valley. Thus, each of the possible gray level values is assigned to the same number of pixels. Adjustment of the brightness values of pixels based on the histogram of the image by equalization is shown in Figure 4b. There are isolated aggregates that in the gray-scale image appear as small white spots in the black background, and there are also aggregates in the objects that are not properly separated and appear as black spots in the white structures. In the absence of a pure image, it is hard to define what noise is, where it is, and what amount of noise is contained in the original noisy image. The proposed methods for reducing speckle noise in the image were the median and low pass filters. By smoothing the characteristics of the filter according to the local statistics around each point of the image, it is possible to suppress the noise while the edges and other important features are preserved. In this study, such filters were adopted to improve the detectability of the aggregate structures and subtle gray-scale variations without affecting the sharpness or internal structure information of the original image. Smoothing was examined with using both the low-pass filter and the median filter to remove the high-spatial frequency noise from a digital image. Noise is often introduced during discretization as a side effect. The smoothing operation enhances the edge of aggregates relative to the background. This is because the background normally has a higher frequency than the edge. A low-pass filter affects the background more than the objects. Thus, it more or less cleans up the background. Median filtering is usually necessary to perform a high degree of noise reduction in an image before edge detection. Comparisons between the median filter and the low pass filter on the image show that the use of the median filter performs better regarding reducing the noise around the edges. The median filter that was proposed in the processing works under the usual assumption that the aggregate in the input image or a region of the input image (boundary of aggregates) has more or less the same intensity in brightness.