## 1 Introduction
[16] proposed a unidirectional total variation model based on an edge-aware weighting to preserve the structure information. Moreover, some methods consider the characteristics of stripe structure or consider image decomposition to obtain clear images from striped images [19; 20; 21; 22; 23]. In [19], the proposed model employed the group sparsity to estimate the stripe component firstly, and used difference-based constraints to describe the direction information of the stripes. In [21], [PERSON] et al. proposed a low-rank-based single-image decomposition model (LRSID) to separate the original image from the stripe component and extended the 2-D image decomposition method to the 3-D case. The deep learning-based methods [24; 25; 26] use a deep convolutional neural network for correction. These methods are fast and effective after the model is built. In [24], [PERSON] et al. proposed an innovative wavelet deep neural network from the perspective of transform and defined a special directional regularizer to separate the scene details from stripe noise. The above methods are mainly used for single-band images without considering spectral correlation, so some methods for multispectral and hyperspectral images are proposed successively [27; 28; 29; 30; 21; 21; 22; 20; 23]. In [27], [PERSON] et al. proposed an unstriped low-dimensional model using the unstriped \"reference\" images, which was then used to derive the destriping transform via linear regression. In [29], [PERSON] et al. proposed a low-rank tensor decomposition framework-based MSI destriping method by decomposing the striped image into the image component and stripe component. For the image component, the author used the spatial UTV and spectral TV regularization. Moreover, for the stripe component, the author adopted tensor Tucker decomposition and 2,1-norm regularization. Although the above of these destriping methods have obtained a satisfactory destriping performance, they do not take into account the stripe characteristics of OHS data (we will analyze the stripe characteristics of OHS in detail in Section 2.2), and the stripes of OHS data are very obvious, non-periodic and randomly distributed. In addition, the stripes are unrelated between different bands. Some bands have very serious stripe noise, while others are weak. Based on the above characteristics of data stripes, we need to consider using some preprocessing method, such as adaptive moment matching in this paper, to reduce the noise of different bands with different noise intensity. The reduction degree of bands with severe noise is large, while that of bands with slight noise is small, which makes the stripe noise level of different bands consistent. After that, uniform parameters are used for further correction. However, the existing methods do not take this situation into account, and make the same parameter correction between different bands, so they are not applicable to OHS images. Therefore, it is necessary to propose a new stripe removal model based on the full consideration of the stripe information of OHS. In order to realize the goal of removing stripe noises and maintaining details for the OHS image, in this paper, we propose a method based upon adaptive moment matching and multi-level unidirectional total variational to remove stripes, and a method based on piecewise linear least squares fit to restore details. Moreover, considering the spectral correlation of hyperspectral images, we extend the algorithm to a hyperspectral case. Besides, we use the split Bregman iteration method to solve the resulting minimization problem. Our approach has been tested on OHS data and compared qualitatively and quantitatively with other destriping methods. The experimental results verify the effectiveness and robustness of our approach. The main contributions of this paper are summarized as follows. 1. We discuss the OHS remote sensing image destriping method for the first time and analyze the stripe noise characteristics and cause in detail. 2.