Antonio Buades

TL;DR: This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach and is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.

TL;DR: Two new methods for the three-dimensional denoising of magnetic resonance images that exploit the sparseness and self-similarity properties of the images are proposed, making them usable in most clinical and research settings.

TL;DR: A new upsampling method is proposed to recover some of this high frequency information by using a data-adaptive patch-based reconstruction in combination with a subsampling coherence constraint to outperform classical interpolation methods in terms of quantitative measures and visual observation.

TL;DR: A novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images by automatically estimating the local noise level present in the image and using it as a guide image within a rotationally invariant non-local means filter.

TL;DR: A new superresolution method is proposed to reconstruct high-resolution images from the low-resolution ones using information from coplanar high resolution images acquired of the same subject.