# Cycle Generative Adversarial Network Based on Gradient Normalization for Infrared Image Generation
## 4 Analysis of Experimental Results
### Experimental Results  
This paper uses objective evaluation metrics to evaluate the quality of generated IR images. The objective evaluation uses the peak signal-to-noise ratio [38; 39] and structural similarity [40; 41] to assess the sharpness and richness of image texture detail. In the field of image generation, structural similarity is a more authoritative image evaluation metric widely used within the current image processing field. It is used to obtain information about the image structure in visible region by the strong correlation between image pixels and to obtain approximate information about the image using whether the perceived structural information has changed to express the similarity difference between images. The peak signal-to-noise ratio is commonly used in image compression, image fusion, and image generation evaluation methods, mainly to compare the difference between two images. A higher signal-to-noise ratio value indicates better quality of the generated image and less difference between images, in which PSNR is defined by the mean square error (MSE). Given a noise-free mn monochrome image I and its noise approximation K, the MSE is defined as shown in Equation (8). \[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\left[I(i,j)-K(i,j)\right]^{2} \tag{8}\] \begin{table}
\begin{tabular}{l c} \hline \hline  & Input: Infrared image / \(l_{i}\)visible image / \(l_{i}\) \\  & Output: False infrared image / \(l_{v}\) generated \\  & from visible image / \(l_{j}\) \\ step 1 & For M epochs do \\ step 2 & For K steps do \\ step 3 & n samples taken from the IR image \\ step 4 & n samples taken from the visible image \\  & distribution / \(\left\{l_{j}^{1},l_{j}^{2},l_{j}^{3},...,l_{j}^{n}\right\}\) \\ step 5 & Training the discriminator Discriminator\_A \\  & and updating the parametric model. \\ step 6 & Training the discriminator Discriminator\_B \\  & and updating the parametric model. \\ step 7 & End for \\ step 8 & Training the generator Generator\_A and \\  & updating the model parameters. \\ step 9 & Training the generator Generator\_B and \\  & updating the model parameters. \\ step 10 & End for \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Training process of the network model. The PSNR is therefore defined as shown in Equation (9). \[PSNR=10\cdot\log_{10}(\frac{MAX_{l}^{2}}{MSE}) \tag{9}\]  
where MAX indicates the possible maximum pixel in the image. The control experiments in this paper were conducted using the same CycleGAN framework structure by implementing control experiments in different generators. The control experiments mainly used UNet_256, ResNet_6 blocks and ResNet_9 blocks, CUT in the two datasets. CUT [42] is proposed in 2020. CUT is a state-of-the-art image translation implemented using contrast learning. CUT does not require paired datasets as training data, and generates clear quality images. CUT is now widely used in the field of image generation. This paper's method comprises ResNet_9 blocks, channel attention, spatial attention mechanism, and gradient normalization. In order to make it easier to read, the method is named GN_CycleGAN, and the method composed of ResNet_9 blocks, channel attention, and spatial attention mechanism is called CBAM_CycleGAN. It will be applied in the later experimental part, and the experimental results are shown in Table 2. The visualized images are shown in Figures 3 and 4. Compared with the original CycleGAN method in the Flir dataset, the PSNR and SSIM metrics of the proposed method in this paper improved by 2.3% and 9.7%, respectively. The PSNR metric improved by 32.2% on the OSU colour thermal dataset, and the SSIM metric improved from 0.2479 to 0.7491. The objective metrics of the two datasets in Data Table 2 show that the quality pf infrared images generated by the method are significantly improved, and based on this proposed method, the desired results in terms of objective evaluation metrics have also been obtained, which objectively validates the effectiveness of algorithm in this paper.