Destriping pushbroom satellite imaging systems with total variation-L1/-L2 method

This paper introduces a variational method for destriping data acquired by pushbroom-type satellite imaging systems. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. It is based on the basic principles of regularization and data fidelity with certain constraints using modern methods in variational optimization — namely total variation (TV), both L 1 and L 2 fidelity, and the alternating direction method of multipliers (ADMM). The main algorithm in this paper, TV-L 1 , uses sparsity promoting energy functionals to achieve two important imaging effects. The TV term maintains boundary sharpness of content in the underlying clean image, while the L 1 fidelity allows for the equitable removal of stripes without over- or under-penalization, providing a more accurate model of presumably independent sensors with unspecified and unrestricted bias distribution. A comparison is made between the TV-L 1 and TV-L 2 models to exemplify the qualitative efficacy of an L 1 striping penalty. The model makes use of novel minimization splittings and proximal mapping operators, successfully yielding more realistic destriped images in very few iterations.