This
letter proposes a novel compressive sensing reconstruction method for correlated
images by using joint regularization,
where a compensation-based
adaptive total variation (CATV) regularization and a multi-image nonlocal
low-rank
(MNLR)
regularization are included. In CATV, local weights
are assigned to the residual values in the gradient domain so as to
constrain the
regularization strength at each pixel. In MNLR, the search of similar patches
goes across different images so that both
self-similarity and inter-image
similarity are explored. Afterward, an efficient algorithm is proposed to solve
the joint formulation,
using a Split-Bregman-based
technique. The effectiveness of the proposed approach is demonstrated with
experiments on both
multiview
images and video sequences.
Index Terms—Compressive sensing, motion estimation/disparity
estimation (ME/DE), nonlocal low-rank regularization (NLR), total variation.
To facilitate further evaluation and
exploration of the method proposed in the above paper, we publish the source
code at this link.
You are free to use the source code provided
that (1) you clearly cite the source; and (2) you do not make any
redistribution of the code.