With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area. However, despite promising results, the field still faces challenges that require further research e.g., allowing flexible upsampling, more effective loss functions, and better evaluation metrics. We review the domain of SR in light of recent advances, and examine state-of-the-art models such as diffusion (DDPM) and transformer-based SR models. We present a critical discussion on contemporary strategies used in SR, and identify promising yet unexplored research directions. We complement previous surveys by incorporating the latest developments in the field such as uncertainty-driven losses, wavelet networks, neural architecture search, novel normalization methods, and the latests evaluation techniques. We also include several visualizations for the models and methods throughout each chapter in order to facilitate a global understanding of the trends in the field. This review is ultimately aimed at helping researchers to push the boundaries of DL applied to SR.

翻译：随着深度学习（DL）的兴起，超分辨率（SR）也成为一个蓬勃发展的研究领域。然而，尽管取得了令人鼓舞的成果，该领域仍面临挑战，需要进一步研究，例如实现灵活的上采样、更有效的损失函数以及更好的评估指标。我们结合近期进展回顾了SR领域，并审视了扩散模型（DDPM）及基于Transformer的SR模型等最先进模型。我们对SR中使用的当代策略进行了批判性讨论，并指出了有前景但尚未探索的研究方向。通过纳入该领域的最新发展，如不确定性驱动损失、小波网络、神经架构搜索、新型归一化方法及最新评估技术，我们对以往综述进行了补充。我们还为每一章中的模型和方法提供了多种可视化展示，以促进对该领域趋势的整体理解。本综述旨在帮助研究人员突破深度学习在超分辨率应用中的边界。