## 3 Results  
A total of 7.3 h was required for training and 61.2 s for testing. These processes were performed on a computer with 64-bit Python, using the Ubuntu operating system with an Intel Xeon E5-2620 processor (2.10 GHz), 32 GB of memory, and an NVIDIA GeForce GTX 1080 Ti graphics card. Figure 6 depicts four typical examples of improvements achieved by our algorithm. The first and second examples are from a wide field of view, while the third and fourth are from a narrow field of view. Each example contains one input image, three output images obtained via real FFA imaging, three output images from our algorithm, three output images from Sequence GAN [19], three output images from pix2 pix [22], and three output images from cycleGAN [23]. As shown in Figure 6, the FFA images translated by our algorithm show higher similarity with real FFA images than those translated by Sequence GAN [19], pix2 pix [22], and cycleGAN [23]. As shown in the third phase images of the first and second examples, the FFA images translated by our algorithm, which are denoted by red square boxes, perform significantly better in the translation of optic nerve head (ONH) leakages. As a result, we qualitatively demonstrate our algorithm's advancements. To further validate our algorithm's advancements, we compare FFA images translated by our algorithm to those translated by state-of-the-art algorithms [19; 22; 23] using PSNR, SSIM [24], and MSE. The PSNR, SSIM [24], and MSE values calculated using the entire final testing results are listed in Table 1. Here, the real FFA images are considered as the ground truth. The PSNR and SSIM values are not very high due to the fluctuations of real three-phase FFA images and limited size of our dataset. Specifically, real three-phase FFA images are fluctuated by some measuring factors, for example, heart rates. Although the limited size of our dataset affects PSNR and SSIM values, these PSNR and SSIM values can also be used to validate our algorithm's advancements. Based on these similarity indices, we identify two facts. First, our algorithm achieves higher mean PSNR and SSIM [24], and lower mean MSE, which indicates that the FFA images translated by our algorithm are overall more similar to real FFA images than those translated by Sequence GAN [19], pix2 pix [22], and cycleGAN [23]. Second, the performances of our algorithm in the three phases are more balanced than those of Sequence GAN [19], which is caused by the unbalanced mutual information utilization rate of Sequence GAN [19]. Thus, the advancements of our algorithm are confirmed. \begin{table}
\begin{tabular}{c c c c c} \hline  & & **PSNR**\(\uparrow\) & **SSIM**\(\uparrow\) & **MSE**\(\downarrow\) \\ \hline \multirow{4}{*}{Our algorithm} & Phase 1 & 16.5551 & 0.4361 & 2206.174 \\  & Phase 2 & 15.8551 & 0.4536 & 2221.589 \\  & Phase 3 & 16.2978 & 0.5338 & 2003.709 \\  & **Mean** & **16.236** & **0.4745** & **2143.824** \\  & Phase 1 & 11.0153 & 0.3299 & 5809.015 \\ Sequence GAN & Phase 2 & 12.8195 & 0.4081 & 3718.937 \\  & Phase 3 & 13.4115 & 0.5466 & 3635.466 \\  & **Mean** & **12.4154** & **0.4282** & **4387.806** \\  & Phase 1 & 13.2272 & 0.3162 & 4163.63 \\ Pix2 pix & Phase 2 & 14.0097 & 0.3882 & 2990.015 \\  & Phase 3 & 14.5526 & 0.5313 & 3171.523 \\  & **Mean** & **13.9298** & **0.4119** & **3441.722** \\  & Phase 1 & 10.0193 & 0.1691 & 7489.193 \\ Cycle GAN & Phase 2 & 10.6604 & 0.2057 & 6165.381 \\  & Phase 3 & 11.713 & 0.3957 & 5422.204 \\  & **Mean** & **10.7975** & **0.2568** & **6358.926** \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the similarity metrics for translated FFA: overall PSNR, SSIM [24], and MSE. Figure 6: Qualitative comparison with state-of-the-art methods using four examples. The first and the second example are from a wide field of view, and the third and fourth examples are from a narrow field of view. The images with red square boxes show our image translation performances of ONH leakages which are markedly better than the other algorithms.