单幅图像超分辨率重建技术研究进展

图像分辨率是衡量一幅图像质量的重要标准. 在军事、医学和安防等领域, 高分辨率图像是专业人士分析问题并做出准确判断的前提. 根据成像采集设备、退化因素等条件对低分辨率图像进行超分辨率重建成为一个既具有研究价值又极具挑战性的难点问题. 首先简述了图像超分辨率重建的概念、重建思想和方法分类; 然后重点分析用于单幅图像超分辨率重建的空域方法, 梳理基于插值和基于学习两大类重建方法中的代表性算法及其特点; 之后结合用于超分辨率重建技术的数据集, 重点分析比较了传统超分辨率重建方法和基于深度学习的典型超分辨率重建方法的性能; 最后对图像超分辨率重建未来的发展趋势进行展望.

Research Progress of Single Image Super-resolution Reconstruction Technology

Image resolution is an important criterion to measure the quality of an image. High-resolution images are a prerequisite for professionals to analyze problems and make accurate judgments in the fields of military, medicine, and security. The super-resolution reconstruction of low-resolution images according to conditions such as imaging acquisition equipment and degradation factors has become a difficult problem that is both valuable and challenging for research. This paper first briefly describes the concept, reconstruction ideas and method classification of image super-resolution reconstruction. Secondly, the spatial methods for single image super-resolution reconstruction are analyzed, and the representative algorithms and their characteristics of the interpolation-based method and learning-based method are sorted out. Then, combined with the data set used for super-resolution reconstruction technology, the performances of traditional super-resolution reconstruction method and typical super-resolution reconstruction method based on deep learning are analyzed and compared. Finally, the future development trend of image super-resolution reconstruction is prospected.