## 1 Introduction  
Hyperspectral imaging combines imaging technology with spectral detection. It involves capturing image data across a wide range of continuous spectral bands. Whileimaging the spatial features of the target, each spatial pixel forms dozens or even hundreds of narrow bands through dispersion to carry out continuous spectral coverage. Hyperspectral images show great potential in the diagnosis of tumors due to their rich spatial and spectral information [1; 2; 3; 4; 5]. Studies have demonstrated that hyperspectral imaging (HSI) can characterize the morphology, content, and physiology of biological tissues non-invasively in order to accurately diagnose and cure diseases [5; 6; 7; 8]. Using the HSI method, [PERSON] et al. [1; 2] succeeded in classifying stomach cancer cells with a high degree of accuracy. Several categorization techniques were applied by [PERSON] et al. [3] to differentiate the tissues of brain tumors. The convolutional neural network was utilized to separate pigmented basal cell carcinoma from a melanocyte tumor, resulting in a reliable classification accuracy [4]. The extent of tumor infiltration has been foreseen by [PERSON] et al. [9] using HSI to image fresh tumor specimens obtained during surgery. Therefore, using hyperspectral imaging to acquire non-invasive and rich image information to achieve an accurate diagnosis of cancer has become an important research direction in the medical field. However, whether it is the classification of tumor tissue or the prediction of tumor expansion, it must be carried out to make sure that the medical imaging system can obtain clear and noise-free hyperspectral images of biological tissue in order to produce accurate diagnostic results. As we all know, hyperspectral images were first used in remote sensing, and there are many accurate and robust algorithms for the strip noise removal of hyperspectral images in remote sensing. However, the methods used in remote sensing cannot be directly applied to medical hyperspectral images due to the disparities in imaging systems and image quality requirements. A higher picture quality is frequently needed for the medical hyperspectral imaging of cells or microscopic biological tissues, and non-uniform noise significantly reduces the precision of medical hyperspectral image diagnosis. Due to variations in imaging techniques, the quality of images produced by various medical hyperspectral imaging systems varies greatly. Therefore, in order to enhance imaging quality, distinct image non-uniformity noise removal algorithms must be developed based on the unique characteristics of each imaging system [10]. The response of the sensor of the spatial dimensional push sweep hyperspectral imaging system used for diagnostics is uneven because of the instability of the manufacturing process. The hyperspectral pictures' imaging quality and spectral correctness have been negatively affected by non-uniformity, which depends on the advancement of effective non-uniformity correction techniques [11]. The fringe noise in the acquired image is a manifestation of the strong non-uniformity of the spectrometer detector. The presence of these noises will impact the detected object's reflection value, which will further affect the extraction of the object's spectrum and reduce the processing work's accuracy, as, consequently, one of the crucial problems in the field of hyperspectral images is the non-uniformity correction of the hyperspectral banding noise [12; 13; 14]. There are two types of hyperspectral picture non-uniformity correcting algorithms. The first is a reference-based non-uniformity correction algorithm, which mainly utilizes two-point and multi-point correction methods, among other techniques. In order to calculate the correction coefficient and remove the fringe component from the tumor hyperspectral image, the reference-based non-uniformity correction algorithm uses an image of a uniform light source or an image without a light source as a reference image.