基础理论研究方面，针对国家在信息的表示、存储与高效处理的理论和方法研究方面的战略需求，本项目以研究混沌压缩感知新模型的基础理论为目标，解决混沌压缩感知研究中若干基础性的关键科学问题，证明采样混沌映射所产生的测量矩阵是否满足验证性条件（如NSP,RIP），分析混沌压缩感知的安全机制，提出分布式环境下的信号恢复优化算法，探索噪音存在下的信号恢复效率，建立一套完整的、统一的混沌压缩感知基础理论框架。在图像处理应用中，考虑混沌初始值、采样间隔、测量次数、噪音概率分布等因素对图像恢复的影响，针对不同的实际需求，设计满足效率和视觉体验的数据处理方案。

From the perspective of theoretical research, according to the national requiremnts on information storage and efficient operations, this project focuses on constructing the novel model of chaotic compressed sensing. This project will solve several critical problems in chaotic compressed sensing and prove that the measurement matrix produced by chaotic signal satisfies the conrresponding conditions(such as NSP and RIP). Then we will also analyze the security of proposed model and further propose the optimation algorithm of reconstruction under distributed environment.In addition, we also consider the effeciency of reconstruction in presence of noise. The final goal of this project is to establish an united theoretical scheme for chaotic compressed sensing. From the perspective of the application in image compression, we will consider the reconstruction result under different initial values, sampling distance, measurement times, probability distribution of noise. Based on different requirements, we will design corresponding models to increase the efficiency and vision satisfaction.