A Hybrid Conditional GAN Design for Image-to-Image Translation Integrating U-Net and ResNet

Image-to-image translation is one of the major image processing tasks in the computer vision field that can be utilized in many types of applications such as style transfer, image enhancement, and more. This study introduces a novel approach for image-to-image translation based on a conditional generator adversarial network with a new hybrid generator architecture that combines the U-Net and ResNet architectures. This combination allows the model to benefit from both of their advantages due to their high compatibility. The discriminator uses the PatchGAN architecture for patch-wise discrimination. The model was evaluated by using the SSIM and PSNR which are standard metrics for image quality evaluation. The results are also compared to previous work that uses the same evaluation criteria and datasets. Furthermore, a public survey was conducted in which the participants were asked to choose the image that most closely resembled the target image between the proposed model and another study. The outcome of both the evaluation metrics and the public survey successfully demonstrated that the proposed image-to-image translation method is superior to that of previous studies.