# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 4 Results and Discussion
### Recognition and Computational Time Analysis for CURE-TSR  
Recognition refers to the ability of a computer system to identify or classify objects, patterns, or features in an image or a video stream. Computational time analysis, on the other hand, refers to the process of measuring the time it takes for a computer system to perform a specific task, such as recognition or classification. This is an important metric in computer vision and pattern recognition as it can impact the overall performance and efficiency of the system. We calculate the traffic sign recognition accuracy and computational time for the CURE-TSR dataset. Note, in Tables 2-6, SD stands for standard deviation (SD), which represents the variation of Top-1 accuracy and computational time for each impact type. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Type/Value** \\ \hline Learning rate & 0.1 \\ \hline Epochs & 55 \\ \hline Batch size & 256 \\ \hline Activation function & ReLU \\ \hline Classifier & Softmax \\ \hline Convolutional layers & 3 \\ \hline Max-pooling & 2 \\ \hline Fully connected layers & 3 \\ \hline \end{tabular}
\end{table} 
Table 1: Various parameters and their type/values for CNN. Figure 7: Epochs vs. L=loss for CURE-OR dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Approach Impact**}} & \multicolumn{4}{c}{**Computational Time for Each Impact**} & \multicolumn{4}{c}{**Total**} \\ \hline
**Approach Impact** & **Blur** & **Dirty** & **Rain** & **Snow** & **SH** & **DK** & **Mean** & **SD** \\ \hline Without Denoising & 46 & 45.66 & 45.8 & 43.3 & 43.2 & 38.7 & **43.7** & **2.78** \\ \hline Embedded median & 50.5 & 43 & 45.19 & 44.3 & 44.4 & 46.6 & **45.6** & **2.64** \\ \hline Embedded Gaussian & **35.6** & **36.9** & **36.5** & **35.8** & **38.5** & **36.4** & **36.6** & **1.03** \\ \hline Traditional median & 46.2 & 51.3 & 54.4 & 43.3 & 44.7 & 43.2 & **47.1** & **4.62** \\ \hline Traditional Gaussian & 41.6 & 51 & 54 & 43.4 & 43.1 & 44.6 & **46.2** & **5** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Computational time (minutes) for traffic sign recognition for different impacts with various denoising approaches in CURE-TSR. Here, mean and SD stand for the mean and standard deviation of the computational time for all impacts. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Impact Levels** & **Darkness** & **Shadow** \\ \hline  & **Mean** & **SD** & **Mean** & **SD** \\ \hline Without impact & 117.7 & 97.52 & 117.7 & 97.52 \\ \hline Level 1 & 85.99 & 71.36 & 108.12 & 89.07 \\ \hline Level 2 & 45.75 & 37.82 & 98.37 & 81.51 \\ \hline Level 3 & 24.49 & 20.21 & 88.91 & 76.86 \\ \hline Level 4 & 13.04 & 10.79 & 79.13 & 74.5 \\ \hline Level 5 & 6.93 & 5.84 & 69.49 & 75.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean and standard deviation of gray level pixel intensities for a particular image having different levels of impacts for shadow and darkness. Here, Levels 1–5 represent the extent of impact starting from extreme low to extreme high. Here, the mean and standard deviation represent the impact of each level on the gray level pixel intensities of that image. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Approach Impact**}} & \multicolumn{4}{c}{**Acc. for Each Impact (\%)**} & \multicolumn{4}{c}{**Total**} \\ \hline
**Approach Impact** & **Blur** & **Dirty** & **Rain** & **Snow** & **SH** & **DK** & **Mean** & **SD** \\ \hline Without denoising & 72.3 & 87.6 & **88.5** & 1.9 & **92** & **89.6** & **71.9** & **31.9** \\ \hline Embedded median & 71.8 & 87.6 & 84.4 & 35.6 & 90.7 & 89.2 & **76.5** & **19.33** \\ \hline Embedded Gaussian & 71.3 & **88.1** & 86.3 & 1.9 & 91.1 & 89.2 & **71.31** & **31.7** \\ \hline Traditional median & **73.3** & 85.3 & 79.5 & **35.6** & 87.8 & 84.8 & **74.3** & **17.9** \\ \hline Traditional Gaussian & 70.3 & 80.4 & 74.9 & 1.9 & 83.3 & 1.9 & **52.1** & **35.7** \\ \hline \hline \end{tabular}
\end{table}