Fuzzy Based Directional Weighted Median Filter for Impulse Noise Detection and Reduction

Abstract

The known median-based denoising methods tends to work well for restoring the images corrupted by random-valued impulse noise with low noise level, but it fails in denoising highly corrupted images. In this paper, a new noise reduction method based on directional weighted median based fuzzy impulse noise detection and reduction method (DWMFIDRM) has been proposed, which has been specially developed for denoising all categories of impulse noise. The contribution of this paper is threefold. The main contribution of the novel impulse noise reduction technique lies in the unification of three different methods; the impulse noise detection phase utilizing the concept of fuzzy gradient values, edge-preserving noise reduction phase based on the directional weighted median of the neighboring pixels and a final filtering step in order to deal with noisy pixels of non-zero degree. Such a unique combination has improved the efficiency of this method for high density noise removal. The experimental results of our proposed method have a significant improvement when compared to other existing filters for high density noise removal. This paper utilizes the concept of fuzzy gradient values. The noise reduction phase that preserves edge sharpness is based on the directional weighted median of neighboring pixels. Final filtering phase is performed only when there is non-zero degree of noise pixels. This phase makes our method more efficient in high noise density. Experimental results show that DWMFIDRM provides a significant improvement on other existing filters.