# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 6 Summary  
Although some scholars also use the low-rank sparse method to remove stripes, most of them remove noise from the image itself, and little attention is paid to the structure of the stripes themselves.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Size** & **WAFT** & **UTV** & **ASSTV** & **VSNR** & **SILR** & **L0** & **LRSUTV** \\ \hline \(512\times 512\) & 0.1196 & 4.9194 & 18.0126 & 9.6413 & 20.2170 & 27.9707 & 20.4003 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Running time of various methods. Units are seconds.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Key Parameter** \\ \hline WAFT & numlev = 2, wavtyp =’ db7’, k = 2.8 \\ \hline UTV & \(\alpha=500,\ \beta=5,\ \omega_{1}=0.003,\ \omega_{2}=0.05,\ \lambda=0.05,\ \text{MaxIter}=150\) \\ \hline ASSTV & \(\lambda_{1}=10,\ \lambda_{2}=60,\ \lambda_{3}=30,\ \gamma=0.5,\ \alpha=8,\ \delta=3,\ \text{Maxiter}=150\) \\ \hline VSNR & \(\text{Eps}=0,\ p=2,\ \text{alpha}=4\text{e}-4,\ \text{maxit}=1000,\ \text{prec}=2\text{e}-3,\ \text{C}=1000\) \\ \hline SILR & \(\delta=0.01,\ \beta=1\text{e}-04,\ \gamma=0.01,\ \lambda_{2}=0.7,\ \lambda_{3}=0.5,\ \tau=0.5,\ \text{MaxIter}=150\) \\ \hline L0 & \(\lambda=10,\ \mu=0.1,\ \beta_{1}=1,\ \beta_{2}=1,\ \beta_{3}=1,\beta_{4}=1,\ \text{MaxIter}=150\) \\ \hline LRSUTV & \(\alpha_{1}=20,\ \alpha_{3}=20,\ \alpha_{4}=4,\ \gamma=0.85,\ \tau=0.5,\ \text{MaxIter}=150\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter settings of various methods being compared.  
Of course, a small number of scholars have considered the structural characteristics of the stripes, but they lack the consideration of the statistical characteristics of the stripes. The stripe removal method we used considers not only the continuity of the image itself, but also the structural and statistical characteristics of the stripes. In terms of parameter adjustment, LRSUTV uses an adaptive scheme for some key parameters, which can automatically adjust the relevant regularization coefficients according to the noise level. This simplifies the adjustment of parameters to some extent. Of course, there are also obvious shortcomings in LRSUTV. First, our method is invalid for tilt stripes. The removal of oblique stripes is occasionally encountered in our work. Secondly, LRSUTV is not currently optimized for parallel computing, so there are inefficient computations. In the next work, we will do some research on the removal of tilted stripes and the improvement of computational efficiency.  
The matlab codes are available online at [[http://www.mdpi.com/2076-3417/10/11/3694/s1](http://www.mdpi.com/2076-3417/10/11/3694/s1)]([http://www.mdpi.com/2076-3417/10/11/3694/s1](http://www.mdpi.com/2076-3417/10/11/3694/s1)).  
Below is a brief introduction to the relevant contributions of the authors to this paper. The author, [PERSON], completed the establishment of the model and the related work of model testing. The author, [PERSON], mainly works on Algorithm Analysis in this paper. The author, [PERSON], is mainly engaged in image analysis. The author, [PERSON], mainly focuses on the acquisition of experimental data and the analysis of experimental data. The work of each of the above authors has played a key role in the successful completion of the article. All authors have read and agree to the published version of the manuscript.  
The authors declare no conflict of interest.