Image super-resolution reconstruction refers to a technique of recovering a high-resolution (HR) image (or multiple images) from a low-resolution (LR) degraded image (or multiple images). Due to the breakthrough progress in deep learning in other computer vision tasks, people try to introduce deep neural network and solve the problem of image super-resolution reconstruction by constructing a deep-level network for end-to-end training. The currently used deep learning models can divide the SISR model into four types: interpolation-based preprocessing-based model, original image processing based model, hierarchical feature-based model, and high-frequency detail-based model, or shared the network model. The current challenges for super-resolution reconstruction are mainly reflected in the actual application process, such as encountering an unknown scaling factor, losing paired LR–HR images, and so on.

中文翻译：

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单图像超分辨率重建研究

图像超分辨率重建是指从低分辨率（LR）退化图像（或多个图像）中恢复高分辨率（HR）图像（或多个图像）的技术。由于深度学习在其他计算机视觉任务中的突破性进展，人们试图引入深度神经网络，并通过构建用于端到端训练的深度网络来解决图像超分辨率重建的问题。当前使用的深度学习模型可以将SISR模型分为四种类型：基于插值的预处理模型，基于原始图像处理的模型，基于分层特征的模型以及基于高频细节的模型，或者共享网络模型。当前超分辨率重建的挑战主要体现在实际应用过程中，