Salt-and-pepper noise removal using modified mean filter and total variation minimization

Mickael Aghajarian; John E. McInroy; Cameron H. G. Wright

doi:10.1117/1.JEI.27.1.013002

4 January 2018 Salt-and-pepper noise removal using modified mean filter and total variation minimization

Mickael Aghajarian, John E. McInroy, Cameron H. G. Wright

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 1, 013002 (January 2018). https://doi.org/10.1117/1.JEI.27.1.013002

Abstract

The search for effective noise removal algorithms is still a real challenge in the field of image processing. An efficient image denoising method is proposed for images that are corrupted by salt-and-pepper noise. Salt-and-pepper noise takes either the minimum or maximum intensity, so the proposed method restores the image by processing the pixels whose values are either 0 or 255 (assuming an 8-bit/pixel image). For low levels of noise corruption (less than or equal to 50% noise density), the method employs the modified mean filter (MMF), while for heavy noise corruption, noisy pixels values are replaced by the weighted average of the MMF and the total variation of corrupted pixels, which is minimized using convex optimization. Two fuzzy systems are used to determine the weights for taking average. To evaluate the performance of the algorithm, several test images with different noise levels are restored, and the results are quantitatively measured by peak signal-to-noise ratio and mean absolute error. The results show that the proposed scheme gives considerable noise suppression up to a noise density of 90%, while almost completely maintaining edges and fine details of the original image.

Citation Download Citation

Mickael Aghajarian, John E. McInroy, and Cameron H. G. Wright "Salt-and-pepper noise removal using modified mean filter and total variation minimization," Journal of Electronic Imaging 27(1), 013002 (4 January 2018). https://doi.org/10.1117/1.JEI.27.1.013002

Received: 12 September 2017; Accepted: 7 December 2017; Published: 4 January 2018

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