# Conditional GAN-Based Two-Stage ISP Tuning Method: A Reconstruction-Enhancement Proxy Framework
## 5 Discussion  
In the methods and experimental results above, we compared the performance of the proposed two-stage proxy and end-to-end proxy methods in various dimensions. Firstly, we compared the differences in proxy performance between two methods. By comparing specific optimized SSIM and HC, we validated the proxy effect of the proposed method on image structure and color, demonstrating that it can better grasp the chromaticity information of images compared to the end-to-end proxy method. Secondly, we compared the tuning effects of two proxy methods and a manual tuning method on downstream tasks such as object detection and noise reduction, and used the evaluation indicators of each task to indicate the tuning effect. The results showed that the proposed proxy method had certain improvements in both object detection and noise reduction tasks compared to the end-to-end proxy method and the manual tuning method. Moreover, due to the more obvious restoration effect of the proposed method on ISP color processing compared to structural restoration, the improvement of the proposed method on object detection tasks compared to end-to-end proxy was greater than that on noise reduction tasks. Meanwhile, the proxy based tuning method significantly improves the tuning effect for CV tasks compared to manual tuning, demonstrating the superiority of the proxy tuning method in CV tasks. Thirdly, we compared the effects of different loss functions and PPM on the proposed two-stage proxy. The experiment showed that by setting a reasonable loss function and adding a PPM module, the proxy performance and downstream tuning performance of the staged proxy can be effectively improved, and the performance can be further improved on the basis of the staged improvement. We can also see that the proposed two-stage proxy method has some room for improvement. In the above experimental results, it is shown, firstly, that compared to the end-to-end proxy method, the proposed two-stage method has significant disadvantages in terms of computational complexity for proxy fitting. This is reflected in the fact that the two-stage proxy needs to learn the reconstruction and enhancement proxy of ISP pipelines separately, and the complexity of the corresponding tuning steps will also increase. Meanwhile, due to the addition of various loss functions, the convergence speed of the proxy has also been reduced, resulting in a decrease in the timeliness of the proposed method compared to the end-to-end method. Secondly, compared to the end-to-end method, the proposed two-stage method does not strictly belong to the black-box method because it utilizes the internal information of the ISP pipeline. When encountering an ISP that is completely black-box to the outside tuner, the two-stage proxy method may have a reduced fitting effect on the ISP pipeline due to the inability to obtain information between ISP modules, resulting in a decrease in tuning performance. Thirdly, due to the limitation of experimental time, this article did not extensively experiment with the improvement of two-stage proxy performance by various loss functions, the tuning performance of more different ISPs, and the tuning effect of more downstream CV tasks. Therefore, based on those above issues, there is still a lot of future research space on the basis of this paper. Firstly, on the basis of the existing network structure, the network structure of the reconstruction and enhancement stages can be improved to achieve similar proxy performance at lower complexity. Secondly, by improving the structure of how the parameters are inputted to the network, on the basis of the existing two-stage proxy method, different types of parameters can also correspond to different types of network structures, thereby further enhancing the proxy performance of the model on different category ISP modules.