Sparse Recovery Based on Transformation Basis of FTT and DWT
12Shanxiong Chen, 1 Zhongshi He , 2Hailing Xiong ,2Xianping Yu ,2Xiaoyan liu
1College of Computer Science, Chongqing University,
Chongqing, 400030, China
2College of Computer and Information Science, Southwest University,
Chongqing ,400716 ,China
3Chongqing city management college
Chongqing 401331, China 

 When the signal in a transform domain is sparse or compressible ,it could be projected to low-dimensional vector utilizing measurement matrix. This projection maintains the information required by reconstructing signal. By research about the basic theories for compressed sensing, this article adopted FFT and DWT as the transform matrix  respectively, the random matrix as the sampling , then analyzed the coherence among them in addition to the sparsity of the sampling signals, explored further the ability of the two methods for recovering signals.

 Sparse Recovery; Incoherence; FFT; Reconstructing Signals

,Shanxiong Chen, Zhongshi He , Hailing Xiong ,Xianping Yu ,Xiaoyan liu, "Sparse Recovery Based on Transformation Basis of FTT and DWT", IJACT: International Journal of Advancements in Computing Technology, Vol. 4, No. 18, pp. 407 ~ 415, 2012