Impulse Noise Reduction in Image Sequences using an Adaptive Weighted Filtering

Mahmoud Saeidi and Ali Nazemipour


Image Sequences Filtering, adaptive fuzzy based image filtering, weighted triangular membership function, Weighted median filter


In this paper, we propose a novel spatio temporal fuzzy based algorithm for noise filtering of image sequences. Our proposed algorithm uses adaptive weights based on a triangular membership function: Symmetrical, Continuous Function. In this algorithm median filter is used to suppress noise. Experimental results show when the images are corrupted by high-density Salt and Pepper noise, our fuzzy based algorithm for noise filtering of image sequences, is much more effective in suppressing noise and preserving edges than the previously reported algorithms such as [1-13]. Indeed, assigned weights to noisy pixels are very adaptive so that it well makes use of correlation of pixels. On the other hand, the motion estimation methods are erroneous and in high-density noise they may degrade the filter performance. Therefore, our proposed fuzzy algorithm doesn’t need any estimation of motion trajectory. The proposed algorithm admissibly removes noise without having any knowledge of Salt and Pepper noise density

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