# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 5 Experimental Results and Discussions
### Simulation Experiment  
In the simulation experiment, we generated the noise image of CFPN with different intensities. In order to restore the fact that CFPN has the characteristics of zero mean Gaussian distribution, we set the mean value \(\mu=0\), \(\sigma=[0{:}20]\) of CFPN to test the denoising performance of various algorithms. In terms of the sCMOS cameras we currently use, almost all the CFPN we encountered presented aperiodic random stripes, and the PRNU is generally around 0.5%. However, some CMOS cameras used in some fields have periodic stripes. Therefore, in the comparison test of algorithms, we also simulated the existence of periodic noise. Next, we compared and evaluated the effectiveness of our method from two aspects: aperiodic and periodic noise. In terms of picture selection, we chose two pictures, one of which was a picture of a solar active area with a rich structure, and the other being a picture of a relatively single structure of the solar sphere. These two types of pictures are the types of images often encountered in solar observation. Aperiodic stripe noise  
In the simulation experiment of aperiodic stripe, we generated a set of random column noise with mean value \(\mu=0\) and standard deviation \(\sigma=[0{:}20]\), and the position of noise is random. From the test results of Figure 9, the WAFT method and ASSTV method corresponding to Figure 9d,g achieved the worst denoising effect, and obvious stripes remained after denoising. Figure 9e has better results than Figure 9d,g, almost removing all stripes, except for some areas with wider stripes. In these areas, UTV showed incomplete stripe removal, with a certain degree of residue. From a visual point of view, several methods corresponding to Figure 9f,h-j achieved the best results. They completely removed all the stripes. Visually, it is difficult for me to distinguish the differences. Figure 9: _Cont._  
Subjective Qualitative Evaluation  
Next, to further distinguish the differences between VSNR, SILR, \(\ell_{0}\) sparse, and LRSUTV's denoising results, we first made a qualitative comparison of the various methods using the difference image formed by the difference between the original image and the denoising result, and the mean cross-track curve of the denoising result. We then used the PSNR and SSIM values of various denoising results for a quantitative comparison. The difference images shown in Figure 10 clearly show the stripe extraction ability of various methods, and whether the various methods damage the original image structure during the stripe extraction process. Figure 10: _Cont._  
Figure 9: The results of various methods for removing stripes in the solar active area. (**a**) Global image with noise; (**b**) Original image of framed area; (**c**) Noise image of framed area; (**d**) WAFT; (**e**) Anisotropic total variation (UTV); (**f**) VSNR; (**g**) ASSTV; (**h**) SILR; (**i**) \(\ell_{0}\) sparse; (**j**) LRSUTV. It can be found from Figure 10b that the stripe extracted by WAFT has a large error compared to Figure 10a, which indicates that the image after WAFT denoising still has a lot of residual noise stripe. This situation can also be observed in Figure 9d. The stripes in Figure 10c,d are similar to those in Figure 10a, but there is a certain degree of residual image structure information in the stripes. This shows that the denoising results of UTV and ASSTV in these residual areas have a certain degree of damage to the original structure. Figure 10e has a high similarity with Figure 10a, but some areas are bright, which will cause the brightness of the VSNR denoised image in this area to be darker than the original image. Figure 10f,g has a very high similarity to Figure 10a, but there is a shift in overall intensity. The shift in intensity causes the brightness and darkness of the denoised image to be different from the original image. Then, the stripe information extracted by our proposed LRSUTV method is the closest to Figure 10a both in structure and intensity.