Xie Q, Sang N
Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430000, China.
College of Biomedical Engineering, South Central University of Nationalities, Wuhan 430000, China, e-mail:
West Indian Med J. 2015 May 11;65(2):271-276. doi: 10.7727/wimj.2014.174.
The goal of super-resolution is to generate high-resolution images from low-resolution input images.
In this paper, a combined method based on sparse signal representation and adaptive M-estimator is proposed for single-image super-resolution. With the sparse signal representation, the correlation between the sparse representation of high-resolution patches and that of low-resolution patches for the identical image is learned as a set of joint dictionaries and a set of high-resolution patches is obtained for high- and low-resolution patches. Then the dictionaries and high-resolution patches are used to produce the high-resolution image for a low-resolution single image.
At the post-processing phase, the adaptive M-estimator, combining the advantages of traditional L and L norms, is used to give further processing for the resultant high-resolution image, to reduce the artefact by learning and reconstitution, and improve the performance.
Three experimental results show the performance improvement of the proposed algorithm over other methods.
超分辨率的目标是从低分辨率输入图像生成高分辨率图像。
本文提出了一种基于稀疏信号表示和自适应M估计器的单图像超分辨率组合方法。利用稀疏信号表示，学习同一图像高分辨率块和低分辨率块稀疏表示之间的相关性作为一组联合字典，并获得高分辨率块和低分辨率块的一组高分辨率块。然后使用这些字典和高分辨率块为低分辨率单图像生成高分辨率图像。
在后期处理阶段，结合传统L范数和L范数优点的自适应M估计器用于对所得高分辨率图像进行进一步处理，通过学习和重构减少伪像并提高性能。
三个实验结果表明所提算法相对于其他方法性能有所提升。
