摘要
将低分辨率图像重建成高分辨率图像是图像处理领域中的一个重要课题。Yang提出一种基于联合字典学习的图像超分辨率重建算法,其算法样本选取与字典训练方法较为复杂。提出一种基于MOD字典学习的图像超分辨率重建新算法,首先采用少量的训练样本代替Yang的大量训练样本,然后使用MOD字典学习算法代替Yang的FFS字典学习算法,最后利用字典对图像进行稀疏表示与重建。实验结果表明,所提出的算法速度较快,并且重建图像的质量较高。
It is an important topic to reconstruct a high resolution image from a low resolution image. Yang proposed an image super-resolution reconstruction algorithm based on the joint dictionary-learning, which needs large samples, and dictionary training methods are complicated. In this paper, a new algorithm of image super-resolution reconstruction based on MOD dictionary-learning is proposed, a small amount of training samples is firstly used to replace large numbers of training samples of Yang＇s, then the MOD dictionary-learning algorithm is used instead of Yang＇s FFS dictionary-learning algorithm, at last, the resulted dictionary is applied to the image sparse representation and super-resolution reconstruction. The experimental results show that the image reconstruction speed is improved greatly with better reconstruction quality.
出处
《图学学报》
CSCD
北大核心
2015年第3期402-406,共5页
Journal of Graphics
基金
国家自然科学基金资助项目(61170327)
国家科技重大专项支持资助项目(2014ZX02502)
关键词
图像处理
图像重建
联合字典
超分辨率重建
MOD
image processing
image reconstruction
joint dictionary
super-resolution reconstruction
MOD
