Hyperspectral image destriping and denoising from a task decomposition view

Abstract

The generalized mathematical model for HSI denoising or destriping lacks stability and uniqueness properties, failing to accurately portray the distribution and effects of stripes. Solutions following such a model would inevitably result in excessive destriping of strip-free areas, leading to the loss of texture detail. To remedy the above deficiencies, we reformulate the destriping task and introduce a novel solution from the task decomposition view. It is broken down into auxiliary sub-tasks involving stripe mask detection, stripe intensity estimation, and HSI restoration, which greatly reduces the difficulty of solving such an ill-posed problem. Based on this, we adopt a sequential multi-task learning framework and propose a stripes location-dependent restoration network, termed SLDR, which integrates the distribution and intensity features of stripes to achieve accurate destriping and high-fidelity restoration. Furthermore, we design a stripe attribute-aware estimator and a weighted total variation loss function to capture the unique properties of stripes and adaptively adjust the restoration weights of striped and non-striped regions. Extensive evaluation and comprehensive ablation studies on synthetic and practical scenes show the effectiveness and superiority of our model and architecture.