TWO METHODS OF ADAPTIVE MEDIAN FILTERING OF IMPULSE NOISE IN IMAGES

Two new methods of adaptive median filtering of impulse noise in images are proposed in the paper. The first method is based on the joint application of iterative processing and transformation of the result of median filtering using the Lorentzian function. The second method uses alternative masks of median filter, calculated using the Euclidean metric. This approach has made it possible to reduce the size of the processed area without the loss in quality for low-intensity noise. The experimental part of the article shows the results of comparison of the performance of the proposed methods with the known methods. We used three different images distorted by impulse noise with pixel distortion probabilities ranging from 1 % up to 99 %. The numerical evaluation of the quality of image denoising based on the peak signal to noise ratio (PSNR) and the structural similarity (SSIM) has shown that the proposed method shows a better result of processing in all the cases considered, as compared with the known approaches. The results obtained in the paper may find wide practical applications when processing satellite and medical imagery, geophysical data, and in other areas of digital image processing.