# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 2 Method Review
### Basic Image Augmentation Methods  
In this work, we further divide basic image augmentation methods into five primary categories in this work. These are pixel transformation, region cropping/padding, dropout, geometric deformation, and domain adaption. The detailed information on pixel transformation methods is shown in Table 1, while the detailed information on methods for the other four categories is shown in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Method** & **Description** \\ \hline \multirow{6}{*}{Filtering} & Blur & Blurs the input image using a random-sized kernel. \\ \cline{2-3}  & GaussianBlur & Blurs the input image using a Gaussian filter with a random kernel size. \\ \cline{2-3}  & MedianBlur & Blurs the input image using a median filter with a random aperture linear size. \\ \cline{2-3}  & MotionBlur & Applies motion blur to the input image using a random-sized kernel. \\ \cline{2-3}  & Sharpen & Sharpens the input image and overlays the result with the original image. \\ \cline{2-3}  & UnsharpMask & Sharpens the input image using unsharp masking processing and overlays the result with the original image. \\ \cline{2-3}  & AdvancedBlur & Blurs the input image using a generalized normal filter with randomly selected parameters. \\ \cline{2-3}  & RingingOvershoot & Creates ringing or overshoot artifacts by convolving image with 2D sinc filter. \\ \cline{2-3}  & Histogram equalization & Equalize & Equalizes the image histogram. \\ \cline{2-3}  & \multirow{2}{*}{CLAHE} & Applies contrast limited adaptive histogram equalization to the input image. \\ \cline{2-3}  & transformation & RandomToneCurve & Randomly changes the relationship between bright and dark areas of the image by manipulating its tone curve. \\ \cline{2-3}  & \multirow{2}{*}{Color brightness contrast transformation} & \multirow{2}{*}{RandomGamma} & \multirow{2}{*}{Adds random Gamma parameters to the image.} \\ \cline{2-3}  & & & RandomBrightness \\ \cline{2-3}  & \multirow{2}{*}{InvertImg} & \multirow{2}{*}{Invert image by subtracting pixel values from 255.} \\ \cline{2-3}  & RandomContrast & Randomly changes contrast of the input image. \\ \cline{2-3}  & \multirow{2}{*}{HueSaturationValue} & Randomly changes hue, saturation, and value of the \\ \cline{2-3}  & & input image. \\ \cline{2-3}  & \multirow{2}{*}{Posterize} & Reduces the number of bits for each color channel. \\ \cline{2-3}  & \multirow{2}{*}{ColorJitter} & Randomly changes the brightness, contrast, and saturation of \\ \cline{2-3}  & & an image. \\ \hline \multirow{6}{*}{Noise injection} & GaussNoise & Applies Gaussian noise to the input image. \\ \cline{2-3}  & MultiplicativeNoise & Multiplies image to random number or array of numbers. \\ \cline{2-3}  & ISONoise & Applies camera sensor noise. \\ \cline{2-3}  & GlassBlur & Applies glass noise to the input image. \\ \cline{2-3}  & ImageCompression & Decreases image using WebP compression method. \\ \cline{2-3}  & JepgCompression & Decreases image using jpeg compression method. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification overview of basic augmentation methods (1).