# Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise
## 3 Proposed Method and Results
### Experiment
Confusion matrices for the recognition of the CIFAR10 base, noisy with 25% randomized impulse noise, are shown in Figure 4. The matrix corresponding to the noisy image base shows that the neural network was not able to qualitatively recognize objects in the images. This confirms the importance of image preprocessing before loading into the neural network classifier. Tables 4-7 present the values of the \(F1_{\text{score}}\) criterion and the Matthews correlation coefficient obtained by cleaning images from random-valued impulse noise of different intensities. The results reflect that the higher quality of image cleaning by the proposed method--the recognition accuracy of the cleaned base is higher by 56.94% compared to the noisy image, and also higher by 6.38%, 1.26%, and 1.17% compared to the methods from [25,27,28], respectively. In some areas that use computer vision for image classification, such gains in accuracy of 1.26% and 1.17% can be of great importance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Base Change Method**} & \multicolumn{2}{c}{**Accuracy, \%**} & \(F1_{\text{score}}\) & \(MCC\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} ** \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Base Change Method** & **Accuracy, \%** & \(\mathbf{F1_{score}}\) & _MCC_ \\ \hline \multirow{4}{*}{Cleaned based on proposed method} & Noised & 17.31 & 0.1016 & 0.0990 \\  & & [28] & 67.87 & 0.6828 & 0.6450 \\ \cline{1-1}  & & [27] & 72.99 & 0.7337 & 0.7018 \\ \cline{1-1}  & & [25] & 73.08 & 0.7350 & 0.7030 \\ \cline{1-1}  & Cleaned based on proposed method & **74.25** & **0.7460** & **0.7158** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Criteria and metrics of recognition accuracy of the CIFAR10 base, previously cleared of 25% random-valued impulse noise. Figure 4: Comparison of recognition results of original and cleaned from 25% random-valued impulse noise images of CIFAR10 test database: (**a**) original database recognition accuracy is 87.99%; (**b**) accuracy of 25% noised database is 17,31%; (**c**) accuracy of denoised base by [28] is 67.87%; (**d**) accuracy of denoised base by [27] is 72.99%; (**e**) accuracy of denoised base by [25] is 73.08%; (**f**) accuracy of denoised base by proposed method is 74.25%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Base Change Method** & **Accuracy, \%** & \(\mathbf{F1_{score}}\) & _MCC_ \\ \hline \multirow{4}{*}{Cleaned based on proposed method} & Noised & 12.31 & 0.5650 & 0.0320 \\  & & [28] & 36.26 & 0.3451 & 0.3026 \\ \cline{1-1}  & & [27] & **46.67** & **0.4610** & **0.4149** \\ \cline{1-1}  & & [25] & 45.40 & 0.4476 & 0.4012 \\ \cline{1-1}  & Cleaned based on proposed method & 46.46 & 0.4600 & 0.4125 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Criteria and metrics of recognition accuracy of the CIFAR10 base, previously cleared of 50% random-valued impulse noise. show the results of the recognition of images noisy with impulse noise, which were cleaned by known methods [25; 27; 28] and the proposed one. As noted earlier, at 1% noise, the recognition accuracy did not differ by a large amount, from which it is concluded that it does not always make sense to remove noise of such intensity from the image. A noise intensity of 10% had already significantly spoiled the image--the neural network showed the class \"deer\" for this image even after cleaning with known methods. At 25% noise, the neural network showed similar results, except that the proposed method outperformed the next hypothesized category by only 17.1%. Noise with an intensity of 50% or more could not be completely removed for high-quality recognition by the system, for all cleaning methods could not cope with such an intensity, but such a dense noise very rarely appears when transmitting images. In general, when cleaning by the proposed method, the neural network recognized the image cleaned by the proposed method as being better by 13.62% and higher.