Ordering Domain Destriping: Co-Solving the Additive and Multiplicative Stripe Components in Remote Sensing Images

As typical structural noise, stripes commonly occur in remote sensing images captured by linear array sensors, which seriously lowers the image quality and hinders the downstream applications. Differing from the conventional methods, this article explores the ability of the ordering domain in separable stripe representation and provides a new perspective for destriping. To enhance the model flexibility to adapt to different types of stripes, the additive and multiplicative stripe components are fully considered and creatively incorporated into the observation model. Based on the additive-multiplicative observation model and the ordering domain transformation, we propose a novel destriping model, called ordering domain destriping (ODD), which constrains the additive and multiplicative stripe components in line with their statistical distribution characteristics. The results obtained on simulated and real striped images show that the proposed method can successfully estimate the latent clean images without losing stripe-like object details in challenging test scenarios, such as mixed additive-multiplicative stripes, wide stripes, and deadlines. The qualitative and quantitative comparisons with six other destriping methods verify the effectiveness and stability of the proposed model.