Multiple-Coil Magnetic Resonance Image Denoising and Deblurring With Nonlocal Total Bounded Variation

One of the complex tasks in image restoration is to restore images under data correlated noise contaminations. In real-time medical imaging scenarios, such as Magnetic Resonance (MR), Ultrasound, Computed Tomography(CT) etc, it is observed that, the data of interest is severely degraded with data dependent noise interventions. A Nonlocal Total Bounded Variation (NLTBV) approach is being proposed in this paper to denoise as well as deblur multiple-coil MR images corrupted by non-central Chi distributed noise and linear Gaussian blur. The energy functional for the restoration model is derived by applying the Maximum A Posteriori (MAP) estimator on the Probability Density Function (PDF) of the non-central Chi distribution. The numerical implementation is performed using the split-Bregman iterative scheme to improve the convergence rate. The proposed model is compared with the other state of the art models in terms of both visual and statistical quantifications to demonstrate it's performance.