# Image-to-Image Translation Using Identical-Pair Adversarial Networks
## 1 Introduction  
Image-to-image translation refers to a constrained synthesis task of automatically transforming an input image to a synthetic image or mapping an input image to the desired output image. There are several applications of image-to-image translation in the fields of image processing, computer graphics, and computer vision, that transform an input image and generate high-resolution RGB images. e.g. image in-painting [1], image colorization [2], image super-resolution [3], image segmentation [4], and image denoising [5]. Recently, convolutional neural networks have been used for various image-to-image translation tasks [6; 7; 8; 9]. The convolutional neural networks were trained to find the mapping from the input image to the transformed image by penalizing the discrepancy between the output and ground-truth images. Those approaches achieved their performance differently depending on cost function designs. Many efforts have been made to design effective losses and to improve the quality of performance by using pixel-wise losses, but they have received only blurry results [7; 8]. Producing sharp and realistic images is an open challenge and requires better approaches. To overcome this challenge, pix2 pix-cGANs [10] trained models for image-to-image transformation by performing adversarial training to distinguish between real and fake images and by using generative adversarial losses to evaluate the discrepancy between a generated image and the real image. According to their results, they claimed that generative losses are more beneficial to the existing CNNs approaches. However, conditional adversarial networks (cGANs) are not stable, and their objective function must depend on a pixel-wise L1 loss function to make the generated output image closer to the ground-truthimage. Though the author claimed to use L1 instead of L2 to avoid blurry issues, their results are still blurry and limited to human vision in the realm of realistic. Perceptual Adversarial Networks (PAN) [11] used a feature-matching loss from the discriminator by adding feature distance to the generative adversarial loss. PAN produces better results but still has problems similar to those of pix2 pix. While the above issues are still existing an issue of unaligned paired image in a dataset has occurred. There is a limit of gathering a pair image for training its tasks such as rain removal and night-to-day. We are not always able to collect pair image of one event in the same location or environment. Unsupervised image-to-image translation (UNIT) [12] is an early approach for translating an image from one domain to another without any corresponding images in two domains. Dual-GAN [13] intended to develop a learning framework for unlabeled image data as unsupervised dual learning. [14] proposed disentangled representations to handle unpaired training data. This approach uses cross-cycle consistency loss. Domain adaptation technique was proposed by [15] to address the domain shift issue by discovering a mapping from the source data distribution to the target distribution. In a study by [16], Cycle-consistent Adversarial Networks (cycleGAN) [16] has a solution to the problem of unpaired-image translation as well. [16] generates input image forward and backward and uses cycle consistent function to verify the outputs. In our approach, we propose iPANs to improve the quality of generated images and to reduce their blurriness. We want realistic and sharper results. The iPANs consist of two networks, an image transformation network T and a discriminative network D. We use U-NET [17] as network T and a perceptual similarity network [18] as the discriminator network D. Therefore, our discriminator requires two input images, for example, a generated image and a ground-truth image. The pix2 pix-cGANs architecture [19] assumed the input image and ground-truth image to be a real pair. However, in our work, a real pair is made up of the duplicate input of the real sample image or ground-truth image.