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Destriping is a classical problem in remote sensing image processing.Although considerable effort has been made to remove stripes, few of theexisting methods can eliminate stripe noise with arbitrary orientations. Thissituation makes the removal of oblique stripes in the higher-level remotesensing products become an unfinished and urgent issue. To overcome thechallenging problem, we propose a novel destriping model which is self-adjustedto different orientations of stripe noise. First of all, the oriented variationmodel is designed to accomplish the stripe orientation approximation. In thismodel, the stripe direction is automatically estimated and then imbedded intothe constraint term to depict the along-stripe smoothness of the stripecomponent. Mainly based on the oriented variation model, a whole destripingframework is proposed by jointly employing an L1-norm constraint and a TVregularization to separately capture the global distribution property of stripecomponent and the piecewise smoothness of the clean image. The qualitative andquantitative experimental results of both orientation and destriping aspectsconfirm the effectiveness and stability of the proposed method.

Oblique Stripe Removal in Remote Sensing Images via Oriented Variation