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A Deep Journey into Super-resolution: A Survey

机译：进入超级分辨率的深度旅程：调查

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摘要

Deep convolutional networks-based super-resolution is a fast-growing field with numerous practical applications. In this exposition, we extensively compare more than 30 state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution. We introduce a taxonomy for deep learning-based super-resolution networks that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based, and adversarial designs. We also provide comparisons between the models in terms of network complexity, memory footprint, model input and output, learning details, the type of network losses, and important architectural differences (e.g., depth, skip-connections, filters). The extensive evaluation performed shows the consistent and rapid growth in the accuracy in the past few years along with a corresponding boost in model complexity and the availability of large-scale datasets. It is also observed that the pioneering methods identified as the benchmarks have been significantly outperformed by the current contenders. Despite the progress in recent years, we identify several shortcomings of existing techniques and provide future research directions towards the solution of these open problems.
机译：基于深度卷积网络的超分辨率是一种快速增长的现场，具有许多实用的应用。在这一博览会中，我们广泛比较了超过30个最先进的超分辨率卷积神经网络（CNNS），超过三种古典和三个最近引入了基准单图像超分辨率的具有挑战性的数据集。我们向深度学习的超分辨率网络引入分类法，将现有方法分为九个类别，包括线性，残差，多分支，递归，渐进性，关注和对抗性设计。我们还在网络复杂性，内存占用，模型输入和输出，学习细节，网络损耗类型和重要的架构差异方面提供比较（例如，深度，跳过连接，过滤器）。进行的广泛评估显示了过去几年的准确性的一致性和快速增长以及模型复杂性的相应提升和大规模数据集的可用性。还观察到当前竞争者被确定为基准的开创性方法已经显着表现出。尽管近年来进展，但我们确定了现有技术的几个缺点，并为未来的研究方向提供了解决这些公开问题的解决方案。

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