## 1 Introduction  
The orbita hyperspectral satellite (OHS) is the world's first hyperspectral satellite that uses surface coating technology for sensors, and it obtained hyperspectral images of the target object by fabricating variable filters evenly on the detector glass and then using the push-broom mode to acquire alone-track images. The wavelength of the fabricated filters is from the visible spectrum to the near-infrared spectrum. Compared with other hyperspectral satellites, OHS has the advantages of combining high spatial resolution, high spectral resolution, and the large swath width, so it breaks through the bottleneck of hyperspectral satellites, and it opens a new era of quantitative remote sensing. However, due to the influence of the CMOS sensor fabricating process, the response of the satellite sensor is non-uniform. Compared with other real remote sensing images, the noise is more serious, which is mainly expressed as the non-periodic stripe noise. Stripes not only reduce the data interpretability and quality, but also restrict the application of the resulting images. Therefore, it is worthwhile to develop algorithms for the correction of the stripe before the succeeding image interpretation processes are performed. In the past decades, the destriping problem has attracted many research interests. According to the data type, the stripe noise removal method can divide into two categories: Single-band image destriping methods, and multispectral image or hyperspectral image destriping methods, and according to the different method type, they can be mainly classified into four categories: The statistical-based methods, the filtering-based methods, the variation-based methods and the deep learning-based methods. The statistical-based methods assume that the distribution of the digital number for each detector is the same, and then adjusts the target distribution to the reference one [1]. These methods mainly include moment matching [2], histogram matching [3; 4] and the improved algorithms based on moment matching [5; 6]. Generally speaking, these methods are the most widely utilized because of the advantages of simplicity, fast processing speed and satisfactory effect on the area where the surface is evenly covered, but the stripe removal effect is not ideal for areas covered by complex ground objects. The filtering-based methods, such as the wavelet-based filter [7; 8; 9], the selective and adaptive filter [10] or the finite impulse response filter [11], etc., remove stripe noises by constructing a filter at a given frequency. These approaches are easy to achieve and can produce good results on georectified images, but for the image with non-periodic stripes, it is impossible to accurately separate the stripes and the images, resulting in a serious loss of image details. Besides, these methods introduce ringing artifacts, which damage the radiometric precision of the data when the input radiance changes abruptly. Variational methods regard stripe removal problems as an ill-posed inverse problem and obtain destriping images by minimizing an energy function. One of the most famous variational methods is the unidirectional total variational (UTV) method [12], and this method has a good destriping effect, but it is easy to lose details at a single scale. On this basis, many improved variational models are proposed [13; 14; 15; 16; 17; 18]. [PERSON] et al. [13] proposed a 1-D variational method to estimate the statistical feature-based guidance, and the guidance information was then incorporated into 2-D optimization to control the image estimation for a reliable result. [PERSON] et al. [14] proposed a variational destriping model that combined unidirectional total variation and framelet regularization. [PERSON] et al. [15] proposed a MODIS stripe removal model that combined moment matching with the variational method. First, part of the stripe was removed by moment matching, and then the remaining stripe was removed by using the unidirectional total variation model. [PERSON] et al.