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. Datasets and codes for evaluation are publicly available at https://github.com/saeed-anwar/SRsurvey.

中文翻译：

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超分辨率深度之旅

基于深度卷积网络的超分辨率是一个快速发展的领域，具有许多实际应用。在本次博览会中，我们广泛比较了 30 多个最先进的超分辨率卷积神经网络 (CNN) 与三个经典数据集和三个最近引入的具有挑战性的数据集，以对单张图像超分辨率进行基准测试。我们为基于深度学习的超分辨率网络引入了一种分类法，将现有方法分为九类，包括线性、残差、多分支、递归、渐进式、基于注意力和对抗性设计。我们还在网络复杂性、内存占用、模型输入和输出、学习细节、网络损失的类型和重要的架构差异（例如，深度、跳过连接、过滤器）方面提供了模型之间的比较。所进行的广泛评估表明，过去几年准确度持续快速增长，同时模型复杂性和大规模数据集的可用性也相应提高。还观察到，被确定为基准的开创性方法明显优于当前的竞争者。尽管近年来取得了进展，但我们确定了现有技术的几个缺点，并为解决这些开放问题提供了未来的研究方向。用于评估的数据集和代码可在 https://github.com/saeed-anwar/SRsurvey 公开获得。还观察到，被确定为基准的开创性方法明显优于当前的竞争者。尽管近年来取得了进展，但我们确定了现有技术的几个缺点，并为解决这些开放问题提供了未来的研究方向。用于评估的数据集和代码可在 https://github.com/saeed-anwar/SRsurvey 公开获得。还观察到，被确定为基准的开创性方法明显优于当前的竞争者。尽管近年来取得了进展，但我们确定了现有技术的几个缺点，并为解决这些开放问题提供了未来的研究方向。用于评估的数据集和代码可在 https://github.com/saeed-anwar/SRsurvey 公开获得。