Medav Filter—Filter for Removal of Image Noise with the Combination of Median and Average Filters

Noise in an image is undesirable to us as it disrupts and degrades the quality of the image. Noise removal is always a difficult task so as edge preservation when the intensity of the disrupted noise in the original image is high. In this paper, we proposed the Medav Filter which is a combination of mean and adaptive median filter that optimally adjusts the level of mask operations according to the noise density. The median filter has good noise removal qualities, but its complexity is undesirable. While the mean filter is unable to remove heavy tailored noise, we see its complexity increases in the presence of noise which is dependent upon the signal. In the Medav Filter, we proposed an algorithm to improve the peak signal–to-noise ratio (PSNR) which eventually improved the signal-to-noise ratio (SNR). We also described an efficient model for image restoration. The analysis of the algorithm and the Medav Filter shows that the complexity, as well as the performance, is improved as compared to the primitive filters.