Sparse representation with learned multiscale dictionary for image fusion

作者：Yin, Haitao^*

来源：Neurocomputing, 2015, 148: 600-610.

DOI：10.1016/j.neucom.2014.07.003

• Sparse representation
• Multiscale dictionary
• Stationary wavelet
• Image fusion

Sparse representation is a powerful tool to describe image, and achieves lots of state of the art results in various image processing areas. The choice of dictionary plays an important role in sparse representation. In this paper, a novel multiscale dictionary learning method is developed based on the multiscale structure of stationary wavelet. In the proposed multiscale dictionary learning method, the sub-dictionaries for all sub-bands of stationary wavelet are learned with a joint learning strategy. The advantages of the joint dictionary learning are that the learned multiscale dictionary can preserve the hierarchical structure of stationary wavelet, and can capture the intrinsic characteristics of images. To discuss the potential of the developed multiscale dictionary learning approach, sparse representation with the learned multiscale dictionary is applied into image fusion. The experimental results on various types of source images demonstrate that the multiscale dictionary based fusion method outperforms the single-scale dictionary based fusion methods, the mutiscale transform based fusion methods and the separated learned multiscale dictionary based fusion method.

• 出版日期2015-1-19
• 单位南京邮电大学