## 1 Introduction
The other is the non-uniformity correction algorithm based on the scene. Further, the scenario-based methods are mainly divided into three categories: statistical optimization, filtering, and neural network. The denoising principle of the transform domain algorithm based on mathematical statistics is to convert the image from the spatial domain to the transform domain by a certain transformation method, then denoise the image in the transform domain, and, finally,reverse-transform the processed result to the spatial domain to complete the denoising [15]. Statistical methods mainly include low-rank and sparse representation [16; 17], principal component analysis [17], and adaptive filtering [18]. These techniques presuppose that the target pixel signal distribution is adjusted to the same reference distribution and that the signal response distribution of each detector is the same. A different kind of approach is based on filtering, which is further separated into global and local filtering. While global filtering offers the advantage of maintaining more of the image's information, local filtering is more computationally efficient. [PERSON] [19] et al. applied bilateral filtering to infrared images, which can effectively smooth image noise. Other filtering methods, such as wavelet-based filters [20; 21; 22], or finite impulse response filters [23], remove fringe noise by constructing filters at a given frequency. In addition, [PERSON] proposed an adaptive wavelet filter to quickly and accurately learn the appropriate wavelet filtering coefficients [24]. [PERSON] proposed a fringe removal algorithm based on wavelet decomposition and gradient equalization [25]. However, no filter can completely accommodate all frequencies of fringe noise, and certain picture components that contain information will unavoidably be filtered out, leading the clean image that results to lose some of its information or generate \"artifacts\". In the field of medical hyperspectral diagnosis, the decline in image quality will lead to the loss of biological tissue image information, which seriously affects the accuracy of subsequent classification diagnosis. Whether based on filtering or statistical methods, denoising algorithms primarily aim to eliminate non-uniform noise from the detector by continuously improving the software algorithm. However, they do not take into account the imaging characteristics of the hyperspectral imaging system, neglecting the mode noise inherent to the instrument itself. As a result, the effectiveness of removing non-uniform noise in practical applications is limited. The third method to remove fringe noise is based on deep learning, which shows each band of the hyperspectral image as an image, so that each band of the image is removed separately. Deep networks in conjunction with prior strategies have been proposed as a few typical methods to eliminate fringe noise from hyperspectral pictures [10; 26; 27], and [PERSON] et al. [28] used a deep neural network based on wavelet transform, which can better separate fringe noise. In addition, there are some classical end-to-end denoising algorithms based on deep learning, such as DNCNN [29], ADNet [30], etc. The deep-learning algorithm relies on samples, and there is no clean image for reference in the actual pathological sample images collected, so it is challenging to accomplish the desired result without homogeneity noise. In summary, since the generation mechanism of non-uniform noise in medical hyperspectral imaging systems has not been analyzed, neither reference-based nor scenario-based correction denoising methods can completely remove non-uniform noise. By analyzing the source of inhomogeneity noise in the spatial dimensional push sweep hyperspectral imaging system, this paper proposes an algorithm for removing inhomogeneity noise in the spatial dimensional push sweep hyperspectral imaging system based on the reference and scene. The following is a summary of this paper's significant contributions:  
1.