Measurement Matrix Optimization via Mutual Coherence Minimization for Compressively Sensed Signals Reconstruction

Table 2

The statistical PSNRs/SSIMs of five experiment runs’ results in reconstructing image Building by different measurement matrices at  = 128.

Times	Sparse random	Gaussian	Bernoulli	Part Hadamard	NoReg	Elad’s	Wang’s	Optimized matrix
1	24.2901/0.44622	24.3995/0.44873	24.0885/0.43630	24.6580/0.47171	24.5528/0.46479	24.7666/0.47067	26.3642/0.58124	26.9759/0.54479
2	23.5791/0.41832	24.4172/0.45150	24.1424/0.44034	24.3653/0.45435	24.5539/0.45525	24.8598/0.47935	25.9501/0.56853	27.0524/0.58232
3	24.0906/0.44499	24.5570/0.45856	24.0547/0.44028	24.8548/0.47995	24.1901/0.44146	24.9355/0.47619	25.9409/0.56286	27.2789/0.58696
4	24.0453/0.43432	24.0803/0.43536	24.3196/0.44327	24.6074/0.46906	23.8855/0.43135	24.9349/0.48395	26.2562/0.56896	27.2887/0.59844
5	24.1129/0.44600	24.5950/0.45270	23.8883/0.45586	25.1650/0.49973	24.1520/0.44606	24.6354/0.46319	26.0366/0.57790	27.6707/0.61185
Means	24.0236/0.43797	24.4098/0.44937	24.0987/0.44321	24.7301/0.47496	24.2669/0.44778	24.8264/0.47467	26.1096/0.57190	27.2533/0.58487
Variances	0.0704/1.4532e-4	0.0412/7.4193e-5	0.0243/5.6149e-5	0.0895/2.7715e-4	0.0822/1.6450e-4	0.0162/6.4530e-5	0.0364/5.6239e-5	0.0734/6.3234e-4