Clustered Compressed Sensing via Bayesian Framework

摘要

This paper provides clustered compressive sensing (CCS) based signal processing using Bayesian framework. Images like magnetic resonanse images (MRI) are usually very weak due to the presence of noise and due to the weak nature of the signal itself. Compressed sensing (CS) paradigm can be applied in order to boost such signal recoveries. We applied CS paradigm via Bayesian framework. That is incorporating the different prior information such as sparsity and the special structure that can be found in such sparse signal improves signal recovery. The method is applied on synthetic and medical images including MRI images. The results show that applying the clustered compressive sensing out performs the non clustered but only sparse counter parts when it comes to mean square error(MSE), pick signal to noise ratio (PSNR) and other performance metrics.