## 3 Experiment
### Column Value  
When removing non-uniform fringes from hyperspectral images using the moment matching approach, the column mean value is frequently employed as a crucial reference index. The precise procedure is adjusting each column's means to the reference column's mean and standard deviation. Since the true column mean value of the image cannot be reflected, the adjusted column mean value curve of the image is roughly a straight line, which alters the actual spectral distribution of the image. In order to assess the degree of spectral distribution retention of the denoised image, this study uses the column mean curve as a subjective evaluation index and compares it to the column mean curve of the original image.  
The mean curve of the original noise-filled image column is frequently too rough, with several sharp points and extraneous peaks and valleys that complicate feature extraction and classification tasks and reduce processing accuracy. Therefore, the main objective of removing non-uniform noise is to remove this extra sharpness, making the curve as smooth as possible while maintaining the original trend, and the secondary objective is to avoid over-smoothing that would result in the loss of the curve's details. The reflectivity value must not deviate too much from the original image in order to accomplish the aforementioned goals. Since medical hyperspectral images have dozens or even hundreds of bands, a single column mean curve for a single-band image can only reflect the non-uniform band noise removal results of a single image. In order to reflect the denoising effect of different non-uniform strip noise removal algorithms on all band images, in the experiment, the spectral dimension average of the column mean values of all bands may accurately indicate the influence of all bands' adjustment. Figure 9 shows the comparison of the mean column mean curve before and after denoising for the whole band.  
The image column mean curves that have been denoised by all approaches are smoother than the original image column mean curves, and the classical denoising method is more effective than the neural network denoising method. According to the analysis, the denoising effect is not immediately apparent because there are not enough distinct reference images for the network to learn from. While the column mean distribution curve is smoothed using the conventional filtering and denoising method, some prominent feature values from the original image are also removed at the same time as the smoothing, which results in the loss of image information. Compared with the original image column mean curve, the image column mean curve after denoising by our method fits the original image column mean curve to the greatest extent, and the actual spectral distribution is best preserved.  
The above subjective visual effects of human eyes directly reflect the effectiveness of non-uniform banding noise removal, thus demonstrating the rationality of the method proposed in this paper for the removal of non-uniform banding noise in the push sweep tumor hyperspectral imaging system. Our goal is to establish a dataset of brain tissue hyperspectral images, and this method will continue to be used in the subsequent images collected.