# Improved CycleGAN for Mixed Noise Removal in Infrared Images
## 3 Experiments
### Ablation Study  
In order to test the effect of different numbers of ResNet-E Blocks on the denoising performance of the network, we conducted a comparison experiment by gradually increasing the number of ResNet-E Blocks in the generator, and the obtained results are shown in Table 2. To control the single variable, all loss functions in this experiment are kept with the original CycleGAN.  
The result shows that as the number of ResNet-E Blocks increases from \(0\) to \(3\), for all three test sets, the PSNR values and the SSIM values are improved, which indicates that the generator we proposed can achieve better denoising performance compared to the original generator. As the number increases from \(3\) to \(6\), the values of the two evaluation indicators are further enhanced, which proves that the structural design of ResNet-E Block effectively avoids the problem of gradient vanishing during the training process, which allows the model to learn more complex feature information and improves the network's ability of removing mixed noise. With the number of ResNet-E Blocks continuing to increase, the parameter of the network gradually increased, but PSNR values and SSIM values of the results were not significantly improved; therefore, under the comp  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Indictors** & **0** & **3** & **6** & **9** \\ \hline \multirow{2}{*}{NO\({}_{\text{L}}\)} & PSNR & 37.443 & 37.811 & 38.575 & 38.776 \\  & SSIM & 0.962 & 0.969 & 0.974 & 0.978 \\ \multirow{2}{*}{NO\({}_{\text{M}}\)} & PSNR & 36.478 & 36.936 & 37.479 & 37.641 \\  & SSIM & 0.952 & 0.958 & 0.967 & 0.973 \\ \multirow{2}{*}{NO\({}_{\text{H}}\)} & PSNR & 34.431 & 35.182 & 35.818 & 36.026 \\  & SSIM & 0.919 & 0.928 & 0.936 & 0.938 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the denoising performance with different numbers of ResNet-E Blocks, where \(0\) denotes the original generator in CycleGAN.  
of training difficulty and denoising effect, we chose to set the number of ResNet-E Blocks in the generator to 6.  
For evaluating the effectiveness of each improvement proposed in this paper, we used the original CycleGAN as the benchmark model to conduct ablation experiments, and the results are shown in Table 3, where \"Base\" denotes the original CycleGAN, \"Improved G + D\" denotes that the network adopts the combination of the generator and the discriminator we designed and \"\(\mathbf{L_{cycle}}\)\" denotes that the network uses the cycle consistency loss we improved.  
It can be seen from the results in Table 3 that each of the improvements proposed in this paper contributes to enhancing the denoising effectiveness of the network for mixed noise. When all of the improvement items are used together, the complete algorithm achieves the best PSNR values and SSIM values on all the three test sets. In comparison with the original CycleGAN, for NO\({}_{\text{L}}\), the PSNR and SSIM of the complete algorithm are improved by 1.132 and 0.01, respectively; for NO\({}_{\text{M}}\), the PSNR and SSIM of the complete algorithm are improved by 1.001 and 0.013, respectively; and for NO\({}_{\text{H}}\), the PSNR and SSIM of the complete algorithm are improved by 0.716 and 0.009, respectively, which proves that our improved network can effectively remove the mixed noise of different intensities while better retaining the detailed information of image.