A General Destriping Framework for Remote Sensing Images Using Flatness Constraint

Abstract

This paper proposes a general destriping framework using flatness constraints, where we can handle various regularization functions in a unified manner. Removing stripe noise, i.e., destriping, from remote sensing images is an essential task in terms of visual quality and subsequent processing. Most of the existing methods are designed by combining a particular image regularization with a stripe noise characterization that cooperates with the regularization, which precludes us to examine different regularizations to adapt to various target images. To resolve this, we formulate the destriping problem as a convex optimization problem involving a general form of image regularization and the flatness constraints, a newly introduced stripe noise characterization. This strong characterization enables us to consistently capture the nature of stripe noise, regardless of the choice of image regularization. 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 image regularizations and stripe noise characterizations using hyperspectral images (HSI) and infrared (IR) videos.Comment: submitted to IEEE Transactions on Geoscience and Remote Sensin