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信号稀疏分解广泛应用于图像和信号处理领域,特别是在数据压缩和数据存储、特征提取领域应用广泛。但信号稀疏分解本身是典型的NP困难问题,要成功的进行信号稀疏分解是十分困难的。文中利用模拟退火算法来快速寻找Matching Pursuit(MP)过程每一步的最优原子,提出了一种基于模拟退火的信号稀疏分解算法。仿真结果表明该算法能有效和快速地进行信号稀疏分解。
Signal sparse decomposition is widely used in the field of image and signal processing, especially in the field of data compression and data storage and feature extraction. However, signal sparse decomposition itself is a typical NP difficult problem, it is very difficult to successfully carry out signal sparse decomposition. In this paper, the simulated annealing algorithm is used to quickly find the optimal atom for each step of the Matching Pursuit (MP) process. A signal sparse decomposition algorithm based on simulated annealing is proposed. Simulation results show that the algorithm can effectively and quickly sparse signal decomposition.