  Destriping is a classical problem in remote sensing image processing.
Although considerable effort has been made to remove stripes, few of the
existing methods can eliminate stripe noise with arbitrary orientations. This
situation makes the removal of oblique stripes in the higher-level remote
sensing products become an unfinished and urgent issue. To overcome the
challenging problem, we propose a novel destriping model which is self-adjusted
to different orientations of stripe noise. First of all, the oriented variation
model is designed to accomplish the stripe orientation approximation. In this
model, the stripe direction is automatically estimated and then imbedded into
the constraint term to depict the along-stripe smoothness of the stripe
component. Mainly based on the oriented variation model, a whole destriping
framework is proposed by jointly employing an L1-norm constraint and a TV
regularization to separately capture the global distribution property of stripe
component and the piecewise smoothness of the clean image. The qualitative and
quantitative experimental results of both orientation and destriping aspects
confirm the effectiveness and stability of the proposed method.
