# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 2 Method Review
### Basic Image Augmentation Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Method** & **Description** \\ \hline \multirow{6}{*}{Region} & \multicolumn{1}{c}{Crop} & \multicolumn{1}{c}{Crops region from image.} \\ \cline{2-3}  & RandomCrop & \multicolumn{1}{c}{Crops a random part of the input.} \\ \cline{2-3}  & CropAndPad & \multicolumn{1}{c}{Crops and pads images by pixel amounts or fractions of image sizes.} \\ \cline{2-3}  & RandomResizedCrop & \multicolumn{1}{c}{Crops a random part of the input and rescales it to some size.} \\ \cline{2-3}  & RandomGridShuffle & \multicolumn{1}{c}{Randomly shuffles grid’s cells on image.} \\ \hline \multirow{6}{*}{Dropout} & PixelDropout & Sets pixels to 0 with some probability. \\ \cline{2-3}  & ChannelDropout & Randomly drops channels in the input image. \\ \cline{2-3}  & CoarseDropout & CoarseDropout of the rectangular regions in the image. \\ \cline{2-3}  & Cutout & CoarseDropout of the square regions in the image. \\ \cline{2-3}  & GridDropout & \multicolumn{1}{c}{Drops out rectangular regions of an image and the corresponding mask in a grid fashion.} \\ \hline \multirow{6}{*}{Geometric deformation} & LongestMaxSize & Rescales an image so that the maximum side is equal to max\_size. \\ \cline{2-3}  & SmallestMaxSize & Rescales an image so that the minimum side is equal to max\_size. \\ \cline{2-3}  & RandomScale & Randomly resizes the input. The output image size is different from the input image size. \\ \cline{2-3}  & Downscale & Decreases image quality by downscaling and upscaling back. \\ \cline{2-3}  & Resize & Resizes the input to the given height and width. \\ \cline{2-3}  & RandomRotate90 & Randomly rotates the input by 90 degrees zero or more times. \\ \cline{2-3}  & Rotate & Rotates the input by an angle selected randomly from the uniform distribution. \\ \cline{2-3}  & SafeRotate & Rotates the input inside the input’s frame by a random angle. \\ \cline{2-3}  & Affine & Applies affine transformations, including translation, rotation, scaling, and shear. \\ \cline{2-3}  & Perspective & Performs a random four-point perspective transform of the input. \\ \cline{2-3}  & OpticalDistortion & Random distortion of images. \\ \cline{2-3}  & ElasticTransform & Elastic deformation of images. \\ \cline{2-3}  & GridDistortion & Mesh warping of images. \\ \cline{2-3}  & Flip & Flips the input either horizontally, vertically, or both horizontally and vertically. \\ \cline{2-3}  & HorizontalFlip & Flips the input horizontally around the y-axis. \\ \cline{2-3}  & VerticalFlip & Flips the input vertically around the x-axis. \\ \hline \multirow{2}{*}{Transpose} & \multicolumn{1}{c}{Transposes the input by swapping rows and columns.} \\ \cline{2-3}  & & & \\ \end{tabular}
\end{table} 
Table 2: Classification overview of basic augmentation methods (2). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Method** & **Description** \\ \hline \multirow{6}{*}{Region} & RandomRain & Adds rain effects. \\ \cline{2-3}  & RandomSnow & Bleaches out some pixel values simulating snow. \\ \cline{2-3}  & RandomSunFlare & Simulates sun flare for the image. \\ \cline{2-3}  & RandomFog & Simulates fog for the image. \\ \cline{2-3}  & RandomShadow & Simulates shadows for the image. \\ \cline{2-3}  & Emboss & Emboses the input image and overlays the result with the original image. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._