图像超分辨率重建是用于提高图像质量的一项重要技术, 得益于深度学习在计算机视觉领域的成功应用和快速发展, 单图像超分辨率重建的效果得到了显著提升. 因此, 本文针对基于深度学习的单图像超分辨率重建方法展开深入研究, 首先综合介绍了用于该领域的基准数据集、性能评价指标、损失函数等相关知识, 然后对有监督学习和无监督学习下单图像超分辨率重建技术的最新算法进行分类讨论, 并且比较分析了不同模型之间的异同点与优缺点, 最后对该领域面临的问题和未来的发展方向进行了总结与展望.
Image super-resolution reconstruction is an important technique to improve image quality. Thanks to the successful application and rapid development of deep learning in the field of computer vision, significant improvement in single image super-resolution (SISR) reconstruction has been achieved. In response, this study explores SISR reconstruction methods based on deep learning in depth. Relevant background knowledge such as benchmark data sets, performance evaluation indexes, and the loss function used in this field are outlined. Then, the latest algorithms for SISR reconstruction techniques with supervised and unsupervised learning are discussed respectively, and the differences and similarities among different models as well as their advantages and disadvantages are compared. Finally, the existing problems in this field are summarized, and future trends are proposed.