ZERO-GRADIENT CONSTRAINTS FOR DESTRIPING OF REMOTE-SENSING DATA

This paper proposes an effective and efficient destriping method for remote-sensing data. Destriping of remote-sensing data is an essential task because stripe noise not only degrades the visual quality but also seriously affects subsequent processing. We formulate the destriping problem as a convex optimization problem involving zero-gradient constraints, where the constraints are designed to exploit the fact that the spatial and temporal gradients of stripe noise equal to zero. Our method imposes such strong constraints on stripe noise, and thus can fully capture the nature of stripe noise, leading to very effective destriping. Also, operations required for handling the zero-gradient constraints in optimization are simple, which enables us to develop an efficient algorithm for solving the problem by a primal-dual splitting method. We demonstrate the advantages of our method over existing methods on destriping experiments using remote-sensing data.

Automated summary

This paper introduces an effective destriping method for remote-sensing data by formulating the problem as a convex optimization problem with zero-gradient constraints. The method fully captures the nature of stripe noise and utilizes a simple operation to develop an efficient algorithm for solving the problem. The advantages of the method are demonstrated through destriping experiments using remote-sensing data.