  We propose an image representation scheme combining the local and nonlocal
characterization of patches in an image. Our representation scheme can be shown
to be equivalent to a tight frame constructed from convolving local bases (e.g.
wavelet frames, discrete cosine transforms, etc.) with nonlocal bases (e.g.
spectral basis induced by nonlinear dimension reduction on patches), and we
call the resulting frame elements {\it convolution framelets}. Insight gained
from analyzing the proposed representation leads to a novel interpretation of a
recent high-performance patch-based image inpainting algorithm using Point
Integral Method (PIM) and Low Dimension Manifold Model (LDMM) [Osher, Shi and
Zhu, 2016]. In particular, we show that LDMM is a weighted
$\ell_2$-regularization on the coefficients obtained by decomposing images into
linear combinations of convolution framelets; based on this understanding, we
extend the original LDMM to a reweighted version that yields further improved
inpainting results. In addition, we establish the energy concentration property
of convolution framelet coefficients for the setting where the local basis is
constructed from a given nonlocal basis via a linear reconstruction framework;
a generalization of this framework to unions of local embeddings can provide a
natural setting for interpreting BM3D, one of the state-of-the-art image
denoising algorithms.
