基于耦合过完备字典的红外云图超分辨率方法

A Method of Infrared Nephogram Super-resolution Based on Coupled Over-completed Dictionary

针对红外云图分辨率低的问题,提出一种基于耦合过完备字典的超分辨率方法。在分析红外云图成像退化模型的基础上,建立了采用稀疏表示理论的超分辨率重构框架,首先随机抽取大量高、低分辨率云图的图像块,组成训练样本,经过字典学习获取针对高、低分辨率云图块的两个字典Dh和Dl,为保证对应的高、低分辨率云图块关于各自的字典具有相似的稀疏表示,提出一种耦合字典学习算法,该算法改变了字典对的更新策略,通过在每一步迭代中交替优化Dh和Dl,得到耦合的过完备字典对;最后对输入的低分辨率红外云图,采用最优正交匹配追踪算法(Optimized Orthogonal Matching Pursuit Algorithm,OOMP),得到满足重构约束的高分辨率云图。实验结果表明,本文方法与其他方法相比,红外云图重构质量有较为明显的改善,而且比同类方法具有更高的计算效率。

For the problem of low-resolution of infrared nephogram, a method of infrared nephogram super-resolution using coupled over-completed dictionary learning is presented. Based on the analysis of infrared nephogram degradation model, a super-resolution reconstruction framework is built with the sparse representation theory. First, consist a training sample by random sampling a large number of high-resolution and low-resolution sample nephogram patches. Then, construct a couple of dictionaries Dh and Di by dictionary training, to ensure that the corresponding high-resolution and low-resolution nephogram patches have a similar sparse representation on their dictionaries. We propose a coupled dictionary training method to change the strategy of dictionary update, and the coupled dictionaries were obtained by alternatively optimizing the Dh and Dl in each step of iterative. Finally, for the inputting low-resolution infrared nephogram, Optimized Orthogonal Matching Pursuit （OOMP） algorithm is used to achieve the high-resolution infrared nephogram which satisfies the reconstruction constraint. Numerous experiments show that the proposed algorithm can get a higher quality reconstructed infrared nephogram. Moreover, the algorithm efficiency is more effective than the other dictionary-learning algorithms.