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Non-Local or patch-based approaches are used in most of the state-of-the-art methods for image denoising. Denoising with regular square patches may cause noise halos around edges, particularly high contrasted edges. In order to overcome this drawback, this work presents an extension of the Non-Local TV framework that effectively exploits the potential local geometric features of the image by replacing the simple square patches with different oriented shapes. We first solve the denoised models of ROF-NLTV with different oriented patch shapes by Split Bregman algorithm. Then, use exponentially weighted aggregation method based on Stein’s unbiased risk estimate to combine the estimators obtained in the first step. The numerical results show our method outperforms some previous ones. Moreover, common noise halos around edges usually observed by denoising with Non-Local TV method are reduced thanks our approach.

Applying compressed sensing (CS) to magnetic resonance imaging (MRI) makes it possible to reconstruct a MR image from undersampled data. Traditional CS based MR image reconstruction schemes only use the signals' sparsity in an appropriate transform domain to reduce sampling rate. This paper proposes a new MR image reconstruction method which utilizes structure features of the image besides sparsity. The proposed method exploits the distribution of the wavelet coefficients' magnitudes and support information to formulate the objective function by minimizing which the image is reconstructed. The objective function consists of the data consistence term and two regularization terms: l1-l2 norm of the groups which contains the coefficients with the unknown support and total variation (TV) norm of the image. The simulation results from real MR images show that the proposed method outperforms the conventional CS based MR image reconstruction method.