In this paper,the observation matrix and reconstruction algorithm of compressed sensing sampling theorem are studied.The advantages and disadvantages of greedy reconstruction algorithm are analyzed.The disadvantages o...In this paper,the observation matrix and reconstruction algorithm of compressed sensing sampling theorem are studied.The advantages and disadvantages of greedy reconstruction algorithm are analyzed.The disadvantages of signal sparsely are preset in this algorithm.The sparsely adaptive estimation algorithm is proposed.The compressed sampling matching tracking algorithm supports the set selection and culling atomic standards to improve.The sparse step size adaptive compressed sampling matching tracking algorithm is proposed.The improved algorithm selects the sparsely as the step size to select the support set atom,and the maximum correlation value.Half of the threshold culling algorithm supports the concentration of excess atoms.The experimental results show that the improved algorithm has better power and lower image reconstruction error under the same sparsely criterion,and has higher image reconstruction quality and visual effects.展开更多
The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers ...The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.展开更多
近年来出现的压缩感知理论为信号处理的发展开辟了一条新的道路,不同于传统的奈奎斯特采样定理,它指出只要信号具有稀疏性或可压缩性,就可以通过少量随机采样点来恢复原始信号。在研究和总结传统匹配算法的基础上,提出了一种新的自适应...近年来出现的压缩感知理论为信号处理的发展开辟了一条新的道路,不同于传统的奈奎斯特采样定理,它指出只要信号具有稀疏性或可压缩性,就可以通过少量随机采样点来恢复原始信号。在研究和总结传统匹配算法的基础上,提出了一种新的自适应正交多匹配追踪算法(adaptive orthogonal multi matching pursuit,AOM-MP)用于稀疏信号的重建。该算法在选择原子匹配迭代时分两个阶段,引入自适应和多匹配的原则,加快了原子的匹配速度,提高了匹配的准确性,实现了原始信号的精确重建。最后与传统OMP算法进行了仿真对比,实验结果表明该算法在重建质量和算法速度上均优于传统OMP算法。展开更多
基金This study was supported by the Yangtze University Innovation and Entrepreneurship Course Construction Project of“Mobile Internet Entrepreneurship”.
文摘In this paper,the observation matrix and reconstruction algorithm of compressed sensing sampling theorem are studied.The advantages and disadvantages of greedy reconstruction algorithm are analyzed.The disadvantages of signal sparsely are preset in this algorithm.The sparsely adaptive estimation algorithm is proposed.The compressed sampling matching tracking algorithm supports the set selection and culling atomic standards to improve.The sparse step size adaptive compressed sampling matching tracking algorithm is proposed.The improved algorithm selects the sparsely as the step size to select the support set atom,and the maximum correlation value.Half of the threshold culling algorithm supports the concentration of excess atoms.The experimental results show that the improved algorithm has better power and lower image reconstruction error under the same sparsely criterion,and has higher image reconstruction quality and visual effects.
基金Acknowledgements This work was supported by the National Natural Science Foundation of China （Grant Nos. 61203321 and 61374135）, China Postdoctoral Science Foundation （2012M521676）, China Central Universities Foundation （106112013CDJZR170005） and Postdoctoral scientific research project of Chongqing special funding （Xm201307）.
文摘The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.
文摘近年来出现的压缩感知理论为信号处理的发展开辟了一条新的道路,不同于传统的奈奎斯特采样定理,它指出只要信号具有稀疏性或可压缩性,就可以通过少量随机采样点来恢复原始信号。在研究和总结传统匹配算法的基础上,提出了一种新的自适应正交多匹配追踪算法(adaptive orthogonal multi matching pursuit,AOM-MP)用于稀疏信号的重建。该算法在选择原子匹配迭代时分两个阶段,引入自适应和多匹配的原则,加快了原子的匹配速度,提高了匹配的准确性,实现了原始信号的精确重建。最后与传统OMP算法进行了仿真对比,实验结果表明该算法在重建质量和算法速度上均优于传统OMP算法。
