DIV8K: DIVerse 8k Resolution Image Dataset

Abstract

Super-resolution methods in literature has in recent years been dominated by convolutional neural networks (CNNs), aiming to learn a direct mapping from a low to high resolution image. Although successful, these methods rely on large-scale and high-quality datasets, to learn more powerful models. The scale of the existing super-resolution datasets are limiting the performance of current deep and highly complex architectures. Moreover, current datasets are severely limited in terms of resolution, prohibiting the move towards more extreme conditions with high upscaling factors. In this paper, we introduce the DIVerse 8K resolution image dataset (DIV8K). The dataset contains a over 1500 images with a resolution up to 8K. It highly covers diverse scene contents. It is therefore the ideal dataset for training and benchmarking super-resolution approaches, applicable to extreme upscaling factors of 32x and beyond. The dataset was employed for the AIM 2019 Image Extreme Super Resolution Challenge.