Clustering and sparse dictionary learning based approximate message passing
Dictionary learning based approximate message passing(AMP)has a high demand on the number of training samples,and its computational cost is high.The double sparse model is introduced to study sparse dictionary learning based AMP,which reduces the demand on the number of training samples in the iterations and improves imaging quality and efficiency.Furthermore,the clustering and sparse dictionary learning based AMP is proposed.In iterations,the clustered blocks are denoised adaptively with sparse dictionary learning.In comparison to traditional dictionary learning based AMP,the clustering and sparse dictionary learning based AMP can achieve 0.20～1.75 dB higher peak signal-to-noise ratio of the reconstructed images,and improve the computational efficiency by 89%in average.
万方数据
维普
