# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 3 Results
### Discussions
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Austenite**} & **Al-La Alloy** \\ \cline{3-4}  & & **VI (\(\downarrow\))** & **IoU (\(\uparrow\))** \\ \hline \multicolumn{4}{c}{**Original**} & **0.0853** & **0.9588** \\ \hline \hline \multirow{9}{*}{Filtering} & Blur & 0.0820 (\(-\)0.0033) & 0.9555 (\(-\)0.0033) \\ \cline{2-4}  & GaussianBlur & 0.0755 (\(-\)0.0098) & 0.9591 (\(+\)0.0003) \\ \cline{2-4}  & MedianBlur & 0.0843 (\(-\)0.0010) & 0.9631 (\(+\)0.0043) \\ \cline{2-4}  & MotionBlur & 0.0820 (\(-\)0.0033) & 0.9591 (\(+\)0.0003) \\ \cline{2-4}  & Sharpen & 0.0856 (\(+\)0.0003) & 0.9575 (\(-\)0.0013) \\ \cline{2-4}  & UnsharpMask & 0.0808 (\(-\)0.0045) & 0.9601 (\(+\)0.0013) \\ \cline{2-4}  & AdvancedBlur & 0.0792 (\(-\)0.0061) & 0.9623 (\(+\)0.0035) \\ \cline{2-4}  & RingingOvershoot & 0.0841 (\(-\)0.0012) & 0.9602 (\(+\)0.0014) \\ \cline{2-4}  & Histogram equalization & Equalize & 0.0958 (\(+\)0.0105) & 0.9581 (\(-\)0.0007) \\ \cline{2-4} Pixel transformation & CLAHE & 0.0818 (\(-\)0.0035) & 0.9590 (\(+\)0.0002) \\ \cline{2-4}  & RandomToneCurve & 0.0833 (\(-\)0.0020) & 0.9603 (\(+\)0.0015) \\ \cline{2-4}  & & Solarize & 0.0766 (\(-\)0.0087) & 0.9581 (\(-\)0.0007) \\ \cline{2-4}  & RandomGamma & 0.0801 (\(-\)0.0052) & 0.9604 (\(+\)0.0016) \\ \cline{2-4}  & Color brightness & RandomBrightness & 0.0778 (\(-\)0.0075) & 0.9579 (\(-\)0.0009) \\ \cline{2-4}  & IntervalImg & 0.0749 (\(-\)0.0104) & 0.9564 (\(-\)0.0024) \\ \cline{2-4}  & RandomContrast & 0.0817 (\(-\)0.0036) & 0.9607 (\(+\)0.0019) \\ \cline{2-4}  & HueSaturationValue & 0.0771 (\(-\)0.0082) & 0.9603 (\(+\)0.0015) \\ \cline{2-4}  & Posterize & 0.0819 (\(-\)0.0034) & 0.9595 (\(+\)0.0007) \\ \cline{2-4}  & ColorJitter & 0.0791 (\(-\)0.0062) & 0.9613 (\(+\)0.0025) \\ \hline \multirow{9}{*}{Noise Injection} & GaussNoise & 0.0754 (\(-\)0.0099) & 0.9627 (\(+\)0.0039) \\ \cline{2-4}  & MultiplicativeNoise & 0.0829 (\(-\)0.0024) & 0.9603 (\(+\)0.0015) \\ \cline{2-4}  & ISONoise & 0.0731 (\(-\)0.0122) & 0.9606 (\(+\)0.0018) \\ \cline{2-4}  & GlassBlur & 0.0833 (\(-\)0.0020) & 0.9561 (\(-\)0.0027) \\ \cline{2-4}  & ImageCompression & 0.0807 (\(-\)0.0046) & 0.9601 (\(+\)0.0013) \\ \cline{2-4}  & JpegCompression & 0.0831 (\(-\)0.0022) & 0.9609 (\(+\)0.0021) \\ \cline{2-4}  & RandomRain & 0.0938 (\(+\)0.0085) & 0.9518 (\(-\)0.0070) \\ \cline{2-4}  & RandomSnow & 0.0885 (\(+\)0.0032) & 0.9565 (\(-\)0.0023) \\ \cline{2-4}  & RandomSunFlare & 0.0855 (\(+\)0.0002) & 0.9598 (\(+\)0.0010) \\ \cline{2-4}  & RandomFog & 0.1008 (\(+\)0.0155) & 0.9537 (\(-\)0.0051) \\ \cline{2-4}  & Random Shadow & 0.0817 (\(+\)0.0036) & 0.9576 (\(-\)0.0012) \\ \cline{2-4}  & Emboss & 0.0721 (\(-\)0.0036) & 0.9533 (\(-\)0.0055) \\ \hline \multirow{9}{*}{Region cropping padding} & CenterCrop & 0.0867 (\(+\)0.0014) & 0.9597 (\(+\)0.0009) \\ \cline{2-4}  & Crop & 0.0766 (\(-\)0.0087) & 0.9586 (\(-\)0.0002) \\ \cline{2-4}  & RandomCrop & 0.0842 (\(-\)0.0011) & 0.9615 (\(+\)0.0027) \\ \cline{2-4}  & CropAndPad & 0.0794 (\(-\)0.0059) & 0.9601 (\(+\)0.0013) \\ \cline{2-4}  & RandomResizedCrop & 0.0912 (\(+\)0.0059) & 0.9580 (\(-\)0.0008) \\ \cline{2-4}  & RandomGridShuffle & 0.0816 (\(-\)0.0037) & 0.9594 (\(+\)0.0006) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of austenite and Al-La alloy in basic augmentation methods (1). 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 brightness-based augmentation method can also improve the performance of image segmentation. As the microscopic imaging process requires external supplementation and adjustment of the light source, we postulate that the brightness-based expansion method can simulate the effects of this procedure on imaging. CLAHE is preferable to global histogram equalization, as the central portion of the image is brighter than the surrounding area in microscopic imaging.