Euclidean-distance-based switching adaptive mean filter for salt-and-pepper noise removal

Image noise will inevitably be produced during the acquisition and transmission of digital images due to environmental interference or device component failure. Salt-and-Pepper Noise (SPN) is a typical sort of image noise. The noisy pixels, which can be described as randomly distributed white and black pixels on the image when SPN corrupts it, have the maximum and minimum values. SPN not only reduces the image quality but also drastically impairs the ability to retrieve image edge details. To enhance the image’s quality, it is crucial to use a top-notch image denoising method. The Euclidean-Distance-based Switching Adaptive Mean Filter (EDSAMeanF) is a new algorithm introduced in this paper. This technique splits the pixels into two groups: noisy pixels and regular pixels. The next step is to process each noisy pixel individually in order. Its templates are built using the Euclidean distance and are adaptive based on the noise density. This method is distinguished by its simple structure, quick execution, and robustness. Finally, we test with 10 common images and evaluated EDSAMeanF against 9 cutting-edge filters in terms of SSIM, MS-SSIM and PSNR. The result reveals that when the noise density is less then 50%, the denoising effect of EDSAMeanF is the state-of-the-art.