Traditionally, the main focus of image super-resolution techniques is on recovering the most likely high-quality images from low-quality images, using a one-to-one low- to high-resolution mapping. Proceeding that way, we ignore the fact that there are generally many valid versions of high-resolution images that map to a given low-resolution image. We are tackling in this work the problem of obtaining different high-resolution versions from the same low-resolution image using Generative Adversarial Models. Our learning approach makes use of high frequencies available in the training high-resolution images for preserving and exploring in an unsupervised manner the structural information available within these images. Experimental results on the CelebA dataset confirm the effectiveness of the proposed method, which allows the generation of both realistic and diverse high-resolution images from low-resolution images.
翻译:传统上,图像超分辨率技术的主要重点是利用一对一的低分辨率和高分辨率绘图,从低质量图像中恢复最有可能的高质量图像。从这个角度出发,我们忽视了这样一个事实,即通常有许多有效的高分辨率图像版本能够映射到给定的低分辨率图像。我们正在这项工作中处理利用基因反反光模型从同一低分辨率图像中获取不同高分辨率版本的问题。我们的学习方法利用高分辨率图像培训中的高频率,以不受监督的方式保存和探索这些图像中的结构性信息。CelebA数据集的实验结果证实了拟议方法的有效性,这种方法使得能够从低分辨率图像中生成现实和多样的高分辨率图像。