Color
demosaicking is used to reconstruct full color images
from incomplete color filter array samples captured by cameras with a single
sensor array. In reconstructing natural-looking images, one key challenge is to
model and respect the statistics of natural images. This paper presents a novel
modeling strategy and an efficient color demosaicking
algorithm. The approach starts with joint modeling of the color images, which
supports simultaneous representation of inter-channel correlation and
structural information in an image. The inter-channel correlation is explored
by measuring the channel difference signals in the gradient domain, while the
structural information is explored by nonlocal low-rank regularization. An
efficient algorithm is then proposed to solve the joint formulation, by
dividing the minimization problem into two sub-problems and solving them
iteratively. The effectiveness of the proposed approach is demonstrated with
extensive experiments on both noiseless and noisy datasets, with comparison
with existing state-of-the-art color demosaicking
methods.
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.