Compressive sensing based on L1 and Hessian regularizations for MRI denoising

Compressive sensing can be used to reduce noise. However, some details also are sparsified. This paper presents a new denoising model based on compressive sensing with L1 and Hessian regularizations for magnetic resonance images denoising. Firstly, the proposed model can make an image more sparse through L1 regularization and reduce noise. Secondly, Hessian regularization is introduced to protect some details from being over-smoothed. Experimental results demonstrate that the proposed method is efficient, and has better denoising capability.