## 4 Experimental Results
As mentioned in Section 3, because the objects for image classification generally had more red and green colors than blue, we determined that in the color perturbation method, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Experiments** \\ \end{tabular} & \begin{tabular}{c} **Method** \\ **Training Images** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Noiseless** \\ \end{tabular} & \
\begin{tabular}{c} **Classification Accuracy** \\ **Noisy** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **\end{tabular} \\ \hline
1 &
\begin{tabular}{c} Training by \\ original images \\ \end{tabular} & 320 & 43.0\% & 29.0\% & 36.5\% \\
3 &
\begin{tabular}{c} **Classification Accuracy** \\ **Training Images** \\ \end{tabular} & 320 & 43.0\% & 29.0\% & 36.0\% \\
3 &
\begin{tabular}{c} **Classification Accuracy** \\ **Training Images** \\ \end{tabular} & 44.0\% & 25.0\% & 34.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification accuracy of the network trained using original images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Experiments** \\ \end{tabular} & \begin{tabular}{c} **Method** \\ **Method** \\ **Training Images** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Noiseless** \\ \end{tabular} & \
\begin{tabular}{c} **Classification Accuracy** \\ **Noisy** \\ \end{tabular} &
\begin{tabular}{c} **Classification Accuracy** \\ **Total** \\ \end{tabular} \\ \hline
1 &
3 &
\begin{tabular}{c} **Classification Accuracy** \\ **Training Images** \\ \end{tabular} & 44.0\% & 25.0\% & 34.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy of the network trained using the conventional method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Experiments** \\ \end{tabular} & \begin{tabular}{c} **Method** \\ **Method** \\ **Training Images** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Noiseless** \\ \end{tabular} & \
\begin{tabular}{c} **Classification Accuracy** \\ **Noisy** \\ \end{tabular} &
\begin{tabular}{c} **Classification Accuracy** \\ **Total** \\ \end{tabular} \\ \hline
1 &
3 &
\begin{tabular}{c} **Images** \\ \end{tabular} & 60.0\% & 44.0\% & 52.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy of the network trained using images obtained through inverse PSNR with RGB color space and rotation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Experiments** \\ \end{tabular} & \begin{tabular}{c} **Method** \\ **Method** \\ **Training Images** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Noiseless** \\ \end{tabular} & \
\begin{tabular}{c} **Classification Accuracy** \\ **Noisy** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **Total** \\ \end{tabular} \\ \hline
1 &
3 &
\begin{tabular}{c} **Classification Accuracy** \\ **Training Images** \\ \end{tabular} & 44.0\% & 25.0\% & 34.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification accuracy of the network trained using original images. a higher weight would most likely result in blue. To provide reasonable evidence, we trained the network using images obtained by applying the inverse PSNR to three channels individually. The classification results are listed in Table 5.  
Comparing the above experimental results, the classification results obtained by applying the inverse PSNR to the B channel were better than those obtained from the images with the inverse PSNR applied to the R and G channels. Therefore, we tuned the weights to increase the weight of the B channel and decrease the weights of the R and G channels. We tested two cases in which the weights were tuned. The results are presented in Table 6.  
As shown in the table, the classification accuracy was higher than that of the network trained by other methods. Specifically, the classification performance on the noisy images was higher than 70%.