Zero-Gradient Constrained Optimization for Destriping of 3D Imaging Data

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.