Impulse noise detection and removal using multiple weighted median filters
Dimitrios Charalampidis, Naga R. Vayuvegula · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
Median filtering has been an effective way for reducing noise of the impulsive kind in images. Yet, the inherent problem with median filters is that their performance may be limited if images are corrupted by a significant amount of noise. In such cases, large median filters may have to be considered, resulting in the removal of fine image details. In order to alleviate this problem, several techniques have been developed and presented in the literature with the purpose of detecting the locations of noisy pixels and applying median filters only at those locations. As a result, image pixels not associated to noise remain unaffected. In the recent past, a method in which noisy pixels were identified based on the information extracted from four directional pixel neighborhoods was proposed. The technique used four directional weighted median filters for processing the detected noisy pixels. It was shown that by considering different directional neighborhoods around each pixel, the fine details of the image, such as thin lines, were preserved, even after filtering was applied. This paper investigates an extension to the previous technique that uses local pixel neighborhoods, in addition to directional ones, which cover a wider spectrum of shapes. The objective of this modification is to increase the possibility of identifying at least one neighborhood which does not cross over fine image details. Comparisons between the original and the proposed method suggest that considering a larger variety of pixel neighborhood shapes is beneficial for impulsive noise detection and removal.