MR images reconstruction based on TV-Group sparse model

Since Magnetic resonance(MR) Images commonly possess a blocky structure and have sparse representations under certain wavelet bases, total variation (TV) and wavelet domain ℓ₁ norm regularization are often enforced together (TV-wavelet method) to improve the recovery accuracy. However, this model ignores that a family of wavelet coefficients has a natural grouping of its components. In this paper, we propose a new TV-Group sparse model which combines TV and wavelet domain group sparse penalty. The corresponding algorithm based on composite splitting method is employed to approach this TV-Group sparse model. Experimental results show that our model can obviously improve both objective and subjective qualities of MR image recovery comparing with the TV-wavelet model.