## 4 Discussion  
Non-reference hyperspectral image denoising is an important branch in the research field of hyperspectral image denoising. Due to the limitation of the imaging equipment, it is difficult to obtain clear and noiseless hyperspectral images. Most of the current denoising methods analyze the possible universal laws of images from the perspective of mathematical statistics, and regard the parts that violate the rules as noise, with a high  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Raw** & **WNNM** & **GDGIF** & **DNCNN** & **ADNet** & **Ours** \\ \hline data 1 & 12.1618 & 27.8579 & 15.2247 & 17.5436 & 19.3572 & 47.4616 \\ data 2 & 13.4947 & 34.0403 & 17.6455 & 13.4007 & 15.2461 & 43.5728 \\ data 3 & 11.4377 & 29.3290 & 18.3316 & 16.2171 & 16.4753 & 47.4629 \\ data 4 & 10.4119 & 33.0236 & 18.7476 & 16.5657 & 16.8221 & 45.2892 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of MICV values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **WNNM** & **GDGIF** & **DNCNN** & **AD-Net** & **Ours** \\ \hline Data01 & 1.1467 & 1.4975 & 0.4163 & 0.4024 & 2.0754 \\ Data02 & 1.0590 & 1.9419 & 0.5636 & 0.5429 & 1.8663 \\ Data03 & 1.0468 & 1.9603 & 0.4808 & 0.4634 & 1.9746 \\ Data04 & 1.0622 & 1.0495 & 0.5071 & 0.4882 & 1.9810 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of MNR values.  
computational complexity and weak generalization ability. In addition, the banding noise caused by different imaging modes of hyperspectral images is also different. Therefore, tracing back to the source, starting from the imaging system itself, and analyzing the reason of instrument noise can result in a better noise reduction effect.  
The lack of noiseless reference images for comparison makes it difficult to conduct research on non-reference hyperspectral image denoising methods because the data that are acquired typically have non-uniform noise. There are various causes of noise production due to the variances between various imaging systems. The reference image in the spatial dimensional push sweep hyperspectral imaging system is the response output (dark background image), which has a good removal effect on the fixed-mode noise. The approach can be considered to remove the non-uniform fringe noise of the spatial dimensional push sweep hyperspectral imaging system, as demonstrated by the denoising index of no reference picture, which indicates that the sample image removed by the dark background image irradiated by no light source has good image quality. A possible limitation is that the non-signal input response output that is currently obtained does not account for temperature changes in the detector. Second, further research is necessary to determine whether variations in the detecting pixel positions across various imaging systems have an impact on the computation of correction factors. Secondly, it is necessary to confirm that the proposed approach is universal for more hyperspectral imaging equipment. As for the random signal noise caused by the imaging process, the filtering method is effective, but it will bring a decline in image quality, although it does not affect the subsequent data processing work, but the visual effect of the human eye changes. Therefore, for the removal of this part of noise, more accurate algorithms need to be explored to better balance the noise removal and visual effects.