# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 2 Method Review
### Basic Image Augmentation Methods
#### 2.1.1 Pixel Transformation  
Pixel transform-based methods mainly change the pixel values of the original image by introducing various pixel value transformations, such as changing the intensity, contrast, and transparency of the image, to produce new samples different from the original image and achieve data augmentation [19]. They mainly include convolutional filtering processing, histogram equalization, color/brightness contrast transformation, and noise injection methods. Pixel transform-based augmentation methods are mainly used for single-image processing, and they have been widely used in various tasks in image processing because of their simplicity and computational efficiency. * Filtering: The filtering method modifies the pixel value of the original image based on the weighted sum of those in the surrounding pixel points [20] and then processes the entire image using a sliding window. By adjusting the size and value of the sliding window, it can create various transformation effects such as image blurring, sharpening, and simple deformation. For instance, Gaussian blur convolves the original image with a Gaussian filter of variable size, producing a new image with varying degrees of blurring. Median blurring is a convolution process using a median filter, which sets the gray value of each pixel of the original image to the median of the gray values of all pixels in the neighborhood of that point. * Histogram equalization: The histogram represents the statistical results of the frequency of different gray values in an image. The gray histogram of an image can visualize the overall gray range, the frequency of different gray values, and the distribution of the image [21]. The contrast of an image is the difference between the highest and lowest brightness in the image, and the greater the difference, the greater the contrast of the image. Histogram equalization refers to the nonlinear stretching of an image with uneven gray distribution so that the frequencies of different gray levels in a certain gray range are approximately equal, and the contrast of the image can be improved by equalization. Ordinary histogram equalization does not fully consider the distribution of gray values in local areas, while contrast limited adaptive histogram equalization, or CLAHE [22], can enhance the local visualization of images while suppressing the effect of noise, and has primarily been applied to the enhancement of medical images. * Color/brightness/contrast transformation: The color/brightness/contrast transformation method alters the image's color space, brightness, contrast, and saturation values at random to generate a new, distinct image from the original image [23]. In image processing, color images can be described using a variety of color spaces, including RGB, HSV [24], YUV, and others. The ColorJitter [25] method can change the color distribution of the original image based on custom values of three input parameters. In addition to color space, brightness, contrast, and saturation are three important parameters of an image that can be used to change imaging effect, such as the RandomBrightness (RandomBrightness) and InvertImg (InvertImg) methods for changing image brightness, and the RandomContrast (RandomContrast) method for changing image contrast. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Method** & **Description** \\ \hline \multirow{3}{*}{Domain adaption} & FDA & Fourier domain adaptation. \\ \cline{2-3}  & \multirow{2}{*}{PDA} & Pixel distribution adaptation. It fits a simple transform (such as PCA, StandardScaler, or MinMaxScaler) on both the original and reference image. \\ \cline{2-3}  & \multirow{2}{*}{HistogramMatching} & Manipulates the pixels of the input image so that its histogram matches that of the reference image. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._* Noise injection [26]: In image segmentation applications, any information that interferes with the extraction of the target of interest is collectively called noise.