Fractal compressive sensing for high-dimension signal recovery
分形压缩感知高维信号重构方法

In the research field of digital signal processing,compressive sensing(CS) becomes more and more important because it changes the traditional signal processing method based on Shannon’s sampling theorem.Under the CS framework,signal recovery is a key point to obtain the digital termination product.The basis pursuit(BP) algorithm seems the most fundamental method of CS recovery,which is essentially an L1-norm minimization problem.However,BP can not be used for the signals with more than one dimension.Therefore,this paper presents a new high-dimension CS recovery method based on fractal dimension theory.The Minkowski dimension is used to replace the L1-norm as an object function in CS recovery.The visualization and SNR of our experimental results show that fractal CS recovery not only inherits the advantage of BP but also improves the dimensional extensive property.