# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 5 Experimental Results and Discussions
### Quantitative Objective Evaluation  
The above analysis is a relatively subjective one. Different people may have different conclusions. Next, we quantitatively give the performance of various methods under different noise intensity and different images in a more objective way. Since we have the data from the original image, we will evaluate the result of the noise reduction using a full-reference approach. The evaluation indexes are: PSNR, SSIM. In Tables 1-4, is the standard deviation of the stripe noise. Pictures of the solar active region add aperiodic noise, and photosphere pictures add periodic noise with a period of 16.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Images** & **Method** & \(\sigma=4\) & \(\sigma=8\) & \(\sigma=12\) & \(\sigma=16\) & \(\sigma=20\) \\ \hline \multirow{6}{*}{Solar Active Region} & WAFT & 31.59958 & 31.34382 & 30.87000 & 30.50006 & 28.54466 \\  & UTV & 31.31853 & 31.16326 & 30.87973 & 30.82699 & 29.55368 \\  & ASSTV & 31.61487 & 31.32085 & 30.76171 & 30.16115 & 27.63940 \\ \cline{1-1}  & VSNR & 29.55233 & 29.45862 & 29.28992 & 29.38995 & 29.41818 \\ \cline{1-1}  & SILR & 32.15904 & 32.06835 & 31.75536 & 31.81308 & 30.89457 \\ \cline{1-1}  & L0 & 31.48390 & 31.21754 & 30.97054 & 31.10174 & 30.95841 \\ \cline{1-1}  & LRSUTV & 32.39316 & 32.19943 & 31.86411 & 31.93278 & 31.05222 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PSNR of denoising results in solar active region by various methods for solar active region.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Images** & **Method** & \(\sigma=4\) & \(\sigma=8\) & \(\sigma=12\) & \(\sigma=16\) & \(\sigma=20\) \\ \hline \multirow{6}{*}{Solar active Region} & WAFT & 33.95483 & 33.70573 & 33.00279 & 31.98996 & 29.77545 \\  & UTV & 33.83339 & 33.79097 & 33.56776 & 33.27398 & 31.70765 \\ \cline{1-1}  & ASSTV & 34.03604 & 33.77074 & 33.04330 & 31.79459 & 29.08905 \\ \cline{1-1}  & VSNR & 32.69314 & 32.72424 & 32.73020 & 32.76216 & 32.39804 \\ \cline{1-1} layer & SILR & 35.43197 & 35.46269 & 35.19106 & 34.89504 & 33.39456 \\ \cline{1-1}  & L0 & 33.79005 & 33.69596 & 33.57622 & 33.38637 & 32.86435 \\ \cline{1-1}  & LRSUTV & 36.80814 & 35.74191 & 35.52385 & 35.42470 & 33.80298 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PSNR of denoising results of various methods for solar granular.  
Figure 15: The power spectrum of the image in the solar active region. (**a**) Original image; (**b**) noise image; (**c**) WAFT; (**d**) UTV; (**e**)VSNR; (**f**) ASSTV; (**g**) SILR; (**h**) \(\ell_{0}\) sparse; (**i**) LRSUTV.  
From Tables 1-4 above, we can see that our proposed LRSUTV method is superior to all the methods on the PSNR index. On the SSIM index, our method is superior to all other methods, except when the variance is 20. This is mainly due to the adaptive adjustment strategy with some parameters in our algorithm and the reasonable model structure. All the methods involved in the comparison adjust their parameters to the best when \(\sigma=12\). Then, they are applied to different noise levels for testing. Experiments show that our method can adapt to different noise levels, and has good robustness.