摘要
经典小波分解对信号稀疏化效果不佳,为此设计了基于小波域经验维纳滤波器的稀疏表示算法.该算法可自适应地衰减每个小波系数,增大系数的稀疏度及可压缩性,从而提高压缩感知算法对信号的恢复质量.仿真结果表明,与传统的基于小波变换的信号稀疏表示及恢复算法相比,该算法较大地提升了对信号及图像的恢复质量.
A wavelet-based Wiener filter is proposed for signal sparse representation since the classical wavelet transform can not posses good sparse results for real signals. The proposed method can adaptively decrease the magnitude of each wavelet coefficient so that sparsity and compressibility of the wavelet coefficients is improved. This results in improvement of recovered signal quality of the compressed sensing algorithm. Simulation results show that, compared to the original sparse representation based on wavelet transform, the proposed algorithm can significantly improve quality of recovered signals for both signals and images.
出处
《应用科学学报》
EI
CAS
CSCD
北大核心
2012年第6期595-600,共6页
Journal of Applied Sciences
基金
国家自然科学基金(No.61272028
No.60802045
No.61073079)
北京市自然科学基金预探索项目基金(No.4113075)
中央高校基本科研业务费专项资金(No.2009JBM028
No.2011JBM223
No.201012200110)
教育部博士点新教师基金(No.20100162120019)
北京交通大学红果园D类人才支持计划资助
关键词
稀疏表示
维纳滤波器
小波变换
正交匹配追踪算法
sparse representation, Wiener filter, wavelet transform, orthogonal matching pursuit （OMP） algorithm
