Impulse Noise Removal Algorithm Using Modified Adaptive Distance-Related Weighted Mean Filter

Abstract

Reduction of impulse noise is a common preprocessing step for many image processing and computer vision applications. Salt-and-pepper noise is a common type of impulse noise which is obtained during image acquisition, storage, and transmission. In such a scenario, the image histogram has a high peak at both ends, leading to a corrupt image caused by the noisy pixels having maximum or minimum intensity values. In the proposed method, an adaptive distance-related weighted mean filter has been implemented to remove impulse noise from a corrupt image. In this technique, each noisy pixel is restored by the distance-related weighted mean of the first occurring non-noisy pixel in every immediate neighboring direction and thus it improves the quality of the image. The proposed technique is tested on standard sample images and it produces better quantitative results than the other existing state-of-the-art algorithms.