  Removing stripe noise, i.e., destriping, from remote sensing images is an
essential task in terms of visual quality and subsequent processing. Most
existing destriping methods are designed by combining a particular image
regularization with a stripe noise characterization that cooperates with the
regularization, which precludes us to examine and activate different
regularizations to adapt to various target images. To resolve this, two
requirements need to be considered: a general framework that can handle a
variety of image regularizations in destriping, and a strong stripe noise
characterization that can consistently capture the nature of stripe noise,
regardless of the choice of image regularization. To this end, this paper
proposes a general destriping framework using a newly-introduced stripe noise
characterization, named flatness constraint, where we can handle various
regularization functions in a unified manner. Specifically, we formulate the
destriping problem as a nonsmooth convex optimization problem involving a
general form of image regularization and the flatness constraint. The
constraint mathematically models that the intensity of each stripe is constant
along one direction, resulting in a strong characterization of stripe noise.
For solving the optimization problem, we also develop an efficient algorithm
based on a diagonally preconditioned primal-dual splitting algorithm (DP-PDS),
which can automatically adjust the stepsizes. The effectiveness of our
framework is demonstrated through destriping experiments, where we
comprehensively compare combinations of a variety of image regularizations and
stripe noise characterizations using hyperspectral images (HSI) and infrared
(IR) videos.
