  This paper proposes an effective and efficient destriping method based on
zero-gradient constraints, which are compatible with various regularization
functions. Removing stripe noise, i.e., destriping, from three-dimensional (3D)
imaging data is an essential task in terms of visual quality and subsequent
processing. Stripe noise has flat structures in the vertical and temporal
directions, meaning that the vertical and temporal gradients are equal to zero.
Exploiting this fact, we first propose a new model for characterizing stripe
noise. Our model constrains the stripe noise gradient to be zero, which we name
the zero-gradient constraint, leading to effective destriping regardless of
what regularization is applied to imaging data. Then, we formulate two types of
convex optimization problems involving the zero-gradient constraints for
destriping and develop efficient solvers for the problems based on a diagonally
preconditioned primal-dual splitting algorithm (DP-PDS). We demonstrate the
advantages of our model through destriping experiments using hyperspectral
images (HSI) and infrared (IR) videos.
