Hyperspectral image destriping and denoising from a task decomposition view

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.