# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 5 Experimental Results and Discussions
### Subjective Qualitative Evaluation  
From the results of Figure 13 stripe extraction, WAFT has the worst effect, and the extracted stripes are not similar to Figure 13a. Figure 13c,d,g are the same as Figure 13a. There is a certain degree of similarity, but the extracted results still carry weak original image structure information. This means that the UTV, ASSTV, \(\ell_{0}\) sparse methods still have streaks left after denoising, but the residual amplitude is not strong enough to make it difficult for the human eye to distinguish. From the streaks in Figure 13e, you can see the periodic trend. However, there is a case where the intensity value is obviously large in some areas, which indicates that the VSNR has a small estimate of the fringes in this area. Figure 13f,h has the highest similarity to Figure 13a, but it can also be clearly seen that the SILR denoising result has the fact that the overall intensity is relatively small. Overall, the results of LRSUTV are closest to the original CFPN, and the effect of stripe extraction is the best.  
Figure 12: Various methods of stripe removal result on the solar sphere image. (**a**) Global image with noise; (**b**) the original image of the comparison area; (**c**) the noise image of the comparison area; (**d**) WAFT; (**e**) UTV; (**f**) VSNR; (**g**) ASSTV; (**h**) SILR; (**i**) \(\ell_{0}\) sparse; (**j**) LRSUTV.  
Figure 13: _Cont._The same conclusion can be found from the analysis of the mean cross-track curve. The curve of Figure 14f-h is most similar to that of Figure 14a, but it can be found that Figure 14f,g has excessive smoothing, which will lead to the loss of detailed information. The current image structure is very simple, so it is difficult to visually detect the difference in their denoising results.  
Figure 14: _Cont._  
Figure 13: Stripes extracted by various methods. (**a**) Added CFPN; (**b**) WAFT; (**c**) UTV; (**d**) ASSTV; (**e**) VSNR; (**f**) SILR; (**g**) \(\ell_{0}\) sparse; (**h**) LRSUTV.  
Because the stripe noise is periodic, we can also compare the power spectrum curves of various denoising results and observe the suppression of noise pulse by various methods. The curve shown in Figure 15 is the power spectrum curve of Figure 12a. For better vision, we normalized the frequency of the \(x\)-axis and logarithmically calculated the power spectrum amplitude of the \(y\)-axis. Due to the periodicity of the noise, the power spectrum curve of Figure 12a shows an obvious pulse signal at some frequencies. After denoising, LRSUTV removes all obvious pulse signals, retains details at maximum range, and maintains the same spectral intensity as the original image. However, WAFT, UTV, ASSTV have distinct large pulse residues, which means striped residues. SILR and \(\ell_{0}\) sparse have significant intensity differences compared with Figure 15a, which means that the overall brightness of the image is different from the original image. The power spectrum on the left side of Figure 15e is significantly different from that of the original, which means that the image after VSNR denoising will have noise residue in the low frequency area.  
Figure 14: Mean cross-track curves of various denoising results, where the green curve in each result is the mean cross-track curve of the original image, and the red curve is the mean cross-track curve of various denoising results. (**a**) Original image; (**b**) WAFT; (**c**) UTV; (**d**) ASSTV; (**e**) VSNR; (**f**) SILR; (**g**) \(\ell_{0}\) sparse; (**h**) LRSUTV.  
Figure 15: _Cont._