  Filtering images of more than one channel is challenging in terms of both
efficiency and effectiveness. By grouping similar patches to utilize the
self-similarity and sparse linear approximation of natural images, recent
nonlocal and transform-domain methods have been widely used in color and
multispectral image (MSI) denoising. Many related methods focus on the modeling
of group level correlation to enhance sparsity, which often resorts to a
recursive strategy with a large number of similar patches. The importance of
the patch level representation is understated. In this paper, we mainly
investigate the influence and potential of representation at patch level by
considering a general formulation with block diagonal matrix. We further show
that by training a proper global patch basis, along with a local principal
component analysis transform in the grouping dimension, a simple
transform-threshold-inverse method could produce very competitive results. Fast
implementation is also developed to reduce computational complexity. Extensive
experiments on both simulated and real datasets demonstrate its robustness,
effectiveness and efficiency.
