Generating image is a hot research topic in the field of deep learning, and it is challenging for generating high quality image pairs. The image pair refers to the corresponding image tuples with the same high-level features and different low-level features, generating high-quality image pairs has important applications in some specific fields. Currently, there are many methods to generate high quality images, but these methods cannot produce higher resolution image pairs. To address this problem, we proposed a novel model which consists of two adversarial variational autoencoders, each one aim at generating an image of pairs more accurately. We called this model CoAdVAE (coupled adversarial variational autoencoders), it can generate high quality image pairs due to introducing adversarial learning to the model. In the experiments, we applied the proposed model to three learning tasks, i.e., generating image pairs with different attributes, converting image attributes, and image dehazing. We show by experiments compared with related approaches on four datasets, Mnist, Celeba, AFHQ, and Fog_data that the proposed model can achieve the-state-of-the-art results.

生成图像是深度学习领域的一个热门研究课题，生成高质量的图像对具有挑战性。图像对是指对应的具有相同高级特征和不同低级特征的图像元组，生成高质量的图像对在某些特定领域具有重要的应用。目前，生成高质量图像的方法有很多，但这些方法都不能生成更高分辨率的图像对。为了解决这个问题，我们提出了一种新模型，该模型由两个对抗变分自编码器组成，每个模型都旨在更准确地生成图像对。我们将此模型称为 CoAdVAE（耦合对抗变分自编码器），由于将对抗性学习引入模型，它可以生成高质量的图像对。在实验中，我们将提出的模型应用于三个学习任务，即生成具有不同属性的图像对、转换图像属性和图像去雾。我们通过实验与四个数据集 Mnist、Celeba、AFHQ 和 Fog_data 上的相关方法进行比较，表明所提出的模型可以达到最先进的结果。