# A Digital Denoising Method Based on Data Frequency Statistical Filtering
## 3 Experiment
### Filtering Effect and Comparison
#### 3.2.2 Low-Density Random Value Impulse Noise Experiment  
When random value impulse noise density is less than 15%, it is denoted as low-density random value impulse noise. Noise densities of 5%, 10%, and 15% are taken as the experimental objects, and the filtering effects of each filtering method are shown in Figure 14.  
Figure 14: _Cont._Figure 14: Cont. In this experiment, a simple mean filtering window of 3 \(\times\) 3 was adopted for mean filtering. From the overall effect, the image is blurred, although there is no obvious noise trace. However, many of the effective details of the image are suppressed and smoothed and, due to the influence of noise on the mean value, the image after denoising still has many small gray jumps. The shadow of noise can still be seen in the image. This phenomenon becomes serious with increases in noise density, resulting in serious image distortion. A simple median filtering window of 3 \(\times\) 3 was adopted for median filtering. From the overall effect, the image appears relatively clear without obvious noise traces. It can be seen from the details that median filtering has a good filtering effect on low-density random impulse noise. Because this is median filtering of the whole image, there is almost no noise omission when noise density is low. However, with increases in noise density, there are some noise omissions, though it is not serious and remains acceptable. In addition, the image denoised by median filtering has the problems of blurring and edge fusion. A 5 \(\times\) 5 window was adopted for switch median filtering. The threshold value of the flat area was 4, and the threshold value of the noise area was 20. From the overall effect, it can be seen that noise point filtering was not complete, and noise point traces were obvious. Moreover, it can be seen from the enlarged figure that the recognition rate of switch median filtering for random impulse noise is very low. When noise density is low, there are still a lot of noise points that cannot be identified. With increases in noise density, more and more noise points are missed. Although the filtered image has good detail retention and less edge blur, the overall image quality is poor. A maximum window of 5 \(\times\) 5 was set for adaptive median filtering. From the overall effect, it can be seen that noise filtering was not complete, and noise traces were obvious. It can be seen from the magnification that, although the recognition rate of the adaptive median filter for random impulse noise is higher than that of the switched median filter, there are still a lot of noise points that cannot be identified, and this phenomenon becomes more serious with increases in density. Although the details of the filtered image are well preserved and the phenomenon of edge blurring is light, the overall image quality is still poor. The main parameters of the filtering method proposed in this experiment are set as follows:  
* Window size: 5 \(\times\) 5;
* Minimum modular units: 5%--12, 10% and 15%--20;
* Data frequency threshold: 5%--0.12, 10%--0.16, 15%--0.18;
* Times filtered: 2;
* Threshold selected by the method: 5%--0.08, 10% and 15%--0.12. Figure 14: Low-density random value impulse noise denoising images. The overall effect of noise filtering is thorough, the image is clear, and there are no obvious noise traces. From the details, it can be seen that this method has a high recognition rate in terms of noise points and has a better processing effect, and the image details are preserved more completely with a resulting image that is less smooth. With increases in noise density, the filtering effect of the experimental filtering method is still good. It has a better denoising effect on low-density noise points, the filtered image is closer to the original image, and the image quality is generally good.