  Diffusion MRI requires sufficient coverage of the diffusion wavevector space,
also known as the q-space, to adequately capture the pattern of water diffusion
in various directions and scales. As a result, the acquisition time can be
prohibitive for individuals who are unable to stay still in the scanner for an
extensive period of time, such as infants. To address this problem, in this
paper we harness non-local self-similar information in the x-q space of
diffusion MRI data for q-space upsampling. Specifically, we first perform
neighborhood matching to establish the relationships of signals in x-q space.
The signal relationships are then used to regularize an ill-posed inverse
problem related to the estimation of high angular resolution diffusion MRI data
from its low-resolution counterpart. Our framework allows information from
curved white matter structures to be used for effective regularization of the
otherwise ill-posed problem. Extensive evaluations using synthetic and infant
diffusion MRI data demonstrate the effectiveness of our method. Compared with
the widely adopted interpolation methods using spherical radial basis functions
and spherical harmonics, our method is able to produce high angular resolution
diffusion MRI data with greater quality, both qualitatively and quantitatively.
