In multidimensional signal processing, such as image and video processing, superresolution (SR) imaging is a classical problem. Over the past 25 years, academia and industry have been interested in reconstructing high-resolution (HR) images from their low-resolution (LR) counterparts. We review the development of SR technology in this tutorial article based on the evolution of key insights associated with the prior knowledge or regularization method from analytical representations to data-driven deep models. The coevolution of SR with other technical fields, such as autoregressive modeling, sparse coding, and deep learning, will be highlighted in both model-based and learning-based approaches. Model-based SR includes geometry-driven, sparsity-based, and gradient-profile priors; learning-based SR covers three types of neural network (NN) architectures, namely residual networks (ResNet), generative adversarial networks (GANs), and pretrained models (PTMs). Both model-based and learning-based SR are united by highlighting their limitations from the perspective of model-data mismatch. Our new perspective allows us to maintain a healthy skepticism about current practice and advocate for a hybrid approach that combines the strengths of model-based and learning-based SR. We will also discuss several open challenges, including arbitrary-ratio, reference-based, and domain-specific SR.

中文翻译：

---

超分辨率图像重建：选择性里程碑和开放问题

在图像和视频处理等多维信号处理中，超分辨率（SR）成像是一个经典问题。在过去的 25 年里，学术界和工业界一直对从低分辨率 (LR) 图像重建高分辨率 (HR) 图像感兴趣。我们根据与先验知识或正则化方法相关的关键见解的演变，在本教程文章中回顾了 SR 技术的发展，从分析表示到数据驱动的深度模型。SR 与其他技术领域（例如自回归建模、稀疏编码和深度学习）的共同演化将在基于模型和基于学习的方法中得到强调。基于模型的 SR 包括几何驱动的、基于稀疏性的、和梯度分布先验；基于学习的SR涵盖三种类型的神经网络（NN）架构，即残差网络（ResNet）、生成对抗网络（GAN）和预训练模型（PTM）。基于模型的 SR 和基于学习的 SR 都从模型数据不匹配的角度强调了它们的局限性，从而将它们结合在一起。我们的新视角使我们能够对当前的实践保持健康的怀疑态度，并倡导结合基于模型和基于学习的 SR 优势的混合方法。我们还将讨论几个开放的挑战，包括任意比率、基于参考和特定领域的 SR。基于模型的 SR 和基于学习的 SR 都从模型数据不匹配的角度强调了它们的局限性，从而将它们结合在一起。我们的新视角使我们能够对当前的实践保持健康的怀疑态度，并倡导结合基于模型和基于学习的 SR 优势的混合方法。我们还将讨论几个开放的挑战，包括任意比率、基于参考和特定领域的 SR。基于模型的 SR 和基于学习的 SR 都从模型数据不匹配的角度强调了它们的局限性，从而将它们结合起来。我们的新视角使我们能够对当前的实践保持健康的怀疑态度，并倡导结合基于模型和基于学习的 SR 优势的混合方法。我们还将讨论几个开放的挑战，包括任意比率、基于参考和特定领域的 SR。