# A Digital Denoising Method Based on Data Frequency Statistical Filtering
## 3 Experiment
### Filtering Effect and Comparison
#### 3.2.3 Medium-Density Random Value Impulse Noise Experiment  
When random value impulse noise density is greater than 15% and less than 50%, it is denoted as medium-density random value impulse noise. Noise densities of 20%, 30%, and 40% were taken as the experimental object. The filtering effect of each filtering method is shown in Figure 16.  
Figure 16: _Cont._  
Figure 16: Medium-density random value impulse noise denoising images. In this experiment, a window of 3 \(\times\) 3 was adopted for mean filtering. From the overall effect, it can be seen that the image is very blurry, and the noise marks are obvious. Many of the effective details in the image are severely suppressed and smoothed. Because noise density was too large, the image details could not be identified, and snowflake noise appeared. This phenomenon becomes serious with increases in noise density, resulting in serious image distortion and a poor denoising effect. A window of 3 \(\times\) 3 was adopted for median filtering. From the overall effect, it can be seen that many noise points have been missed in the image. With increases in noise point density, the omission phenomenon becomes more serious, and the image has obvious noise points. From the details, it can be seen that median filtering has a poor filtering effect on random value impulse noise of medium density. As noise density was too large, noise was more likely to be output as the median value; with increases in noise density, this phenomenon is also aggravated. In addition, most of the details in the image after median filtering appear to have been excessively smoothed with blurred edges. Overall, the denoising effect is relatively general. A 5 \(\times\) 5 window was adopted for switch median filtering, and the thresholds for the flat area and noise area were 5 and 18, respectively. From the overall effect, it can be seen that the noise filtering effect is very poor, and noise marks are obvious. It can be seen from the enlarged figure that the recognition rate of switch median filtering for random impulse noise is very low and, with increases in noise density, more and more noise points are missed. Image details cannot be identified, thus the denoising effect is very poor. A maximum window of 5 x 5 was set for adaptive median filtering. The overall noise filtering effect was not thorough, as noise omission was very present. From the enlarged picture, it can be seen that although the recognition rate of the adaptive median filter for random impulse noise was higher than that of the switched median filter, there are still a large number of noise points that cannot be identified, and this phenomenon becomes more serious with increases in density. The details in the filtered image are hard to recognize, the phenomenon of blurred edges is heavy, and overall image quality is not good. The main parameters of the filtering method proposed in this experiment were set as follows:  
* Window size: 5 \(\times\) 5;
* Minimum modular units: 20%--20, 30% and 40%--24;
* Data frequency threshold: 20%--0.16, 30%--0.24, 40%--0.32;
* Times filtered: 3;
* Threshold selected by the method: 20%, 30%, and40%--0.12. The overall noise filtering effect was more thorough and the image was clear without obvious noise traces. From the details, it can be seen that this method has a high recognition rate for medium-density noise and a good processing effect. However, with increases in noise density, there is a certain blurring phenomenon. Compared to other filtering methods, the blurring is lighter and there is less image smoothing. The denoising effect is good and noises are hardly visible in the details of the image. The filtered image is closer to the original image, and overall image quality is better.