Progressive Hyperspectral Image Destriping With an Adaptive Frequencial Focus.

Limited by the imaging paradigm, stripes are pervasive in remote sensing scenes, and its intensity, density, and periodicity differ dramatically among different imaging systems. Worse, it always coexists with random noises caused by unstable imaging condition. However, current destriping methods are victim to undue ideal assumptions and fail to accurately eliminate stripes against diverse practical degradation, yielding excessive or inadequate destriping results. This study proposes a progressive hyperspectral destriping method with an adaptive frequency focus for accurate destriping and delicate restoration. Specifically, a hierarchical decomposition and reconstruction framework based on progressive wavelet learning encodes the degraded input to the frequency domain with smaller scales, easing the difficulty of restoration. Then, to avoid excessive or insufficient destriping, we devote specific efforts to finely separating noise and preserving details in the high-frequency domain. First, we devise a gradient-aware frequency attention block based on the prominent unidirectional pattern of stripes, empowering to adaptively assign weights according to their sensitivity to the spatial gradient. Second, we design a focal high-frequency loss item that is dynamically scaled according to the feature distance in the high-frequency domain, profiting in identifying and preserving details. Extensive experiments conducted on data with synthetic stripes and realistic satellite scenes validate the superiority of the proposed method over the current state-of-the-art methods. The code is available at https://github.com/EtPan/PHID.