Random noise suppression using adaptive threshold in Shearlet domain
XUE Lin1,2, CHENG Hao1,2, GONG Enpu1,2, CHEN Yijun1,2
1. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, Liaoning 110004, China; 2. School of Resources and Civil Engineering, Northeastern University, Shenyang, Liaoning 110004, China
Abstract：Due to its optimal sparse representation and multi-scale and multi-direction characters,the Shearlet transform has a good performance for seismic noise reduction.Conventional Shearlet-based thresholding method involved the scale of sparsity,but it does not involve the direction of sparsity.It means that noise cannot be efficiently removed.We investigate the signal variation with directions in Shearlet domain and present scale and direction adaptive thresholding based on scale adaptive thresholding.The L2 norm is calculated on a same scale in different directions to investigate the distribution of effective signals.The direction adaptive term is added to thresholding to realize scale and direction adaptive thresholding simultaneously.Model and real data tests show that this simultaneous adaptive thresholding exhibits better performance than the conventional method in random noise reduction and the utmost of signal preservation.
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