基于深度学习的单帧图像超分辨率重建技术

Single-frame image super-resolution reconstruction technology based on deep learning

超分辨率重建技术将低分辨率图像通过算法重建成高分辨率图像。深度学习方法已经在超分辨率重建中取得了显著的进展,文章综述了基于深度学习的单帧图像超分辨率重建技术。首先,介绍了超分辨率重建的研究背景及意义、传统方法的缺陷,以及常见的公开数据集。然后,阐述了近年来基于残差网络及注意力机制的单帧图像超分辨率重建技术等研究内容。最后,对基于深度学习的超分辨率重建技术进行了展望与总结,虽然当前已经取得了一些进展,但仍然面临很多挑战,如模型的泛化能力不足、复杂场景下的超分辨率重建等问题。随着深度学习技术的不断发展和改进,超分辨率重建技术将会有更加广泛的应用。

Super-resolution is the process of reconstructing high-resolution images from low-resolution images through algorithms.Deep learning has made significant progress in super-resolution reconstruction.In this paper,we review single image super-resolution based on deep learning.First,the research background and significance of super-resolution and the shortcomings of traditional methods are introduced.Then,common public datasets are presented,followed by an explanation of recent research on single image super-resolution based on residual networks and attention mechanisms.Finally,this article provides a prospect and summary of super-resolution based on deep learning.Although some progress has been made,many challenges remain,such as the lack of model generalization ability and super-resolution in complex scenes.With the continuous development and improvement of deep learning,super-resolution will have wider applications.