# Improved CycleGAN for Mixed Noise Removal in Infrared Images
## 3 Experiments
### Comparison Test  
In order to further verify the denoising effect of the improved network, we compared it with five current state-of-the-art algorithms in the field of infrared image denoising, which are BM3D (Block Matching and 3D collaborative filtering), DnCNN (denoising convolutional neural network) [31], FFDNet (fast and flexible denoising convolutional neural network) [32], CBDNet (convolutional blind denoising network) [33] and RIDNet (real image denoising network) [34], and the experimental results are shown in Table 4.  
The denoising effects of each comparison algorithm are shown in Figures 4-6, and in order to compare them more conveniently on an intuitive visual level, we present some of the details zoomed in.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**NO\({}_{\text{L}}\)**} & \multicolumn{2}{c}{**NO\({}_{\text{M}}\)**} & \multicolumn{2}{c}{**NO\({}_{\text{H}}\)**} \\ \hline  & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM \\ BM3D & 37.015 & 0.956 & 35.270 & 0.951 & 34.064 & 0.925 \\ DnCNN & 36.832 & 0.946 & 34.886 & 0.937 & 33.418 & 0.905 \\ FFDNet & 37.241 & 0.967 & 36.439 & 0.954 & 33.955 & 0.921 \\ CBDNet & 37.595 & 0.964 & 36.351 & 0.955 & 34.704 & 0.927 \\ RIDNet & 38.172 & 0.971 & 36.944 & 0.962 & 35.413 & **0.939** \\
**Ours** & **38.575** & **0.974** & **37.479** & **0.967** & **35.818** & 0.936 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the comparison test. Bold data indicates the best value in the current group.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Base** & **Improved G + D** & \(\mathbf{L_{cycle}}\) & **PSNR** & **SSIM** \\ \hline  & \(\surd\) & & & 37.443 & 0.962 \\ NO\({}_{\text{L}}\) & \(\surd\) & \(\surd\) & & 38.213 & 0.966 \\  & \(\surd\) & \(\surd\) & \(\surd\) & 38.006 & 0.968 \\  & \(\surd\) & \(\surd\) & \(\surd\) & **38.575** & **0.974** \\  & \(\surd\) & & & 36.478 & 0.952 \\ NO\({}_{\text{M}}\) & \(\surd\) & \(\surd\) & & 37.224 & 0.956 \\  & \(\surd\) & & \(\surd\) & 37.107 & 0.961 \\  & \(\surd\) & \(\surd\) & \(\surd\) & **37.479** & **0.967** \\  & \(\surd\) & & & 34.431 & 0.919 \\ NO\({}_{\text{H}}\) & \(\surd\) & \(\surd\) & & 35.751 & 0.926 \\  & \(\surd\) & & \(\surd\) & 35.632 & 0.931 \\  & \(\surd\) & \(\surd\) & \(\surd\) & **35.818** & **0.936** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the ablation study. Bold data indicates the best value in the current group.  
Figure 4: Comparison of denoising effects among different methods on the test set of \(\text{NO}_{\text{L}}\).  
Figure 5: Comparison of denoising effects among different methods on the test set of \(\text{NO}_{\text{M}}\).  
Figure 6: Comparison of denoising effects among different methods on the test set of \(\text{NO}_{\text{H}}\).  
From the results in Table 4, it can be seen that the improved network achieves optimal PSNR and SSIM results for both test sets, \(\text{NO}_{\text{L}}\) and \(\text{NO}_{\text{M}}\). And for \(\text{NO}_{\text{H}}\), the improved network achieved optimal PSNR and sub-optimal SSIM results. From Figure 4, it can be seen that for low intensity mixed noise, the improved network and other comparative algorithms achieve better denoising results than the DnCNN algorithm in terms of visual perception. As can be seen from Figure 5, the improved network has better denoising effect and detail retention ability than the other algorithms for the data with medium intensity mixed noise. From the comparison of g and h in Figure 6, it is shown that although RIDNet achieves the optimal SSIM values on \(\text{NO}_{\text{H}}\), it causes the loss of image texture information. Instead, the improved network achieves noise removal while retaining the detail information well. These results indicate that the improved network we presented can effectively remove the mixed noise in different intensity cases, and the denoising performance is better than with other comparative algorithms.