1.School of Information Science and Engineering, Central South University, Changsha 410083 2.Engineering Research Center of Electric Power and Traffic Safety Monitoring and Control and Energy Conservation Technology, Ministry of Education, Changsha University of Science and Technology, Changsha 410004 3.School of Sciences, Beijing University of Civil Engineering and Architecture, Beijing 100044
Abstract：To obtain the sparsest representation of an image using a redundant dictionary is NP-hard, and the existing sub-optimal algorithms for solving this problem such as matching pursuit (MP) are highly complex. An image sparse decomposition algorithm based on multi-population discrete differential evolution for multi-component Gabor dictionaries is proposed. Three sub-populations are adopted to search the best matching atoms in different sub-dictionaries, and the correlation coefficient is used to solve overlap-matching in updating process of residual image. To maintain the population diversity, several mutation operators are employed to generate the offspring population in the proposed algorithm. Experimental results show that the sparse approximation performances of the proposed algorithm are comparable with fast matching pursuit (FMP) algorithm. Meanwhile, the computation speed is improved. The proposed algorithm obtains competitive performance compared with other sparse representation methods based on evolution algorithm. Finally, the rationality of the parameters setting in the proposed algorithm is verified by result analysis.
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2014, Vol. 27 
