# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 3 Results
### Discussions
The local histogram equalization method can overcome the difficulty in global histogram equalization by limiting contrast and enhancing the regional area. This method is particularly suitable for materials with austenite-like grain organization. On the contrary, Invertlmg, despite being related to the brightness-based augmentation method, is not applicable to changes that may occur in real-world scenarios for Al-La alloy images, resulting in inferior performance in segmentation experiments. The noise injection method can introduce various types of noise in the microscopic image to simulate the interference of the imaging system. The extra noise can be roughly divided into two categories: normal noise and physical simulation noise. The former includes Gaussian noise, glass noise, and noise brought by image format compression. The latter can simulate real-world phenomena such as rain, fog, solar flares, and shadows. Physical simulation noise impairs performance rather than correctly simulating changes that might occur in real-world scenarios. Compared to background noise, this has a more detrimental effect on two materials' image segmentation. As a result, it greatly reduces the deep learning model's inference capability in both data sets. \begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Method**} & **Austenite** & **Al-La Alloy** \\ \cline{3-4}  & & **VI (\(\downarrow\))** & **IoU (\(\uparrow\))** \\ \hline \multirow{5}{*}{Dropout} & **Original** & **0.0853** & **0.9588** \\ \cline{2-3}  & PixelDropout & 0.0806 (\(-\).0047) & 0.9602 (\(+\)0.0014) \\ \cline{2-3}  & ChannelDropout & 0.0787 (\(-\).0066) & 0.9586 (\(-\)0.0002) \\ \cline{2-3}  & CoarseDropout & 0.0780 (\(-\)0.0073) & 0.9601 (\(+\)0.0013) \\ \cline{2-3}  & Cutout & 0.0812 (\(-\)0.0041) & 0.9613 (\(+\)0.0025) \\ \cline{2-3}  & GridDropout & 0.0823 (\(-\)0.0030) & 0.9609 (\(+\)0.0021) \\ \hline \multirow{5}{*}{Dropout} & LongestMaxSize & 0.0826 (\(-\)0.0027) & 0.9611 (\(+\)0.0023) \\ \cline{2-3}  & SmallestMaxSize & 0.0891 (\(+\)0.0038) & 0.9607 (\(+\)0.0019) \\ \cline{2-3}  & RandomScale & 0.0819 (\(-\)0.0034) & 0.9573 (\(-\)0.0015) \\ \cline{2-3}  & Downscale & 0.0779 (\(-\)0.0074) & 0.9541 (\(-\)0.0047) \\ \cline{2-3}  & Resize & 0.0891 (\(+\)0.0038) & 0.9586 (\(-\)0.0002) \\ \cline{2-3}  & RandomRotate90 & 0.0717 (\(-\)0.0136) & 0.9607 (\(+\)0.0019) \\ \cline{2-3}  & Rotate & 0.0735 (\(-\)0.0118) & 0.9641 (\(+\)0.0053) \\ \cline{2-3}  & SafeRotate & 0.0850 (\(-\)0.0003) & 0.9526 (\(-\)0.0062) \\ \cline{2-3}  & Affine & 0.0704 (\(-\)0.0149) & 0.9610 (\(+\)0.0022) \\ \cline{2-3}  & Perspective & 0.0851 (\(-\)0.0002) & 0.9602 (\(+\)0.0014) \\ \cline{2-3}  & OpticalDistortion & 0.0797 (\(-\)0.0056) & 0.9613 (\(+\)0.0025) \\ \cline{2-3}  & ElasticTransform & 0.0834 (\(-\)0.0019) & 0.9614 (\(+\)0.0026) \\ \cline{2-3}  & GridDistortion & 0.0736 (\(-\)0.0117) & 0.9622 (\(+\)0.0034) \\ \cline{2-3}  & Flip & 0.0732 (\(-\)0.0121) & 0.9594 (\(+\)0.0006) \\ \cline{2-3}  & HorizontalFlip & 0.0692 (\(-\)0.0161) & 0.9622 (\(+\)0.0034) \\ \cline{2-3}  & VerticalFlip & 0.0751 (\(-\)0.0102) & 0.9620 (\(+\)0.0032) \\ \cline{2-3}  & Transpose & 0.0726 (\(-\)0.0127) & 0.9640 (\(+\)0.0052) \\ \hline \end{tabular}
\end{table} 
Table 6: Results of austenite and Al-La alloy in basic augmentation methods (2). The \(\downarrow\) means the lower value, the better performance, and vice versa of \(\uparrow\). The values in parentheses represent the difference between the current results and the original model. The use of combined data augmentation techniques, such as color-jitter, which enhances brightness, contrast, saturation, and hue; and image flipping, which includes both horizontal and vertical flipping, generally results in higher performance than single augmentation. However, when performing combined optimization, it is important to consider the degree of improvement for each component. Automated data augmentation methods based on AutoAugment can significantly reduce the time required for manual search.