Sparse Representations of Images by a Multi-component Gabor Perception Dictionary
基于Gabor 感知多成份字典的图像稀疏表示算法研究

It is currently a hot research topic that how to design an effective over-complete dictionary matching various geometric structures of images to provide sparse representation of images. A multi-component Gabor perception dictionary matching various image structures is constructed in terms of geometric properties of the local structures and the perception character of HVS. Furthermore, an effective algorithm based on the matching pursuit method is proposed to obtain sparse decomposition of images with our dictionary. The experimental results indicate that the Gabor multi-component perception dictionary can adaptively provide a precise and complete characterization of local geometry structures, such as plain, edge and texture in images. In comparison with the anisotropic refinement-Gaussian (AR-Gauss) mixed dictionary, our dictionary has a much sparser representation of images.