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Cognition and Removal of Impulse Noise With Uncertainty

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1 Author(s)
Zhe Zhou ; State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China

Uncertainties are the major inherent feature of impulse noise. This fact makes image denoising a difficult task. Understanding the uncertainties can improve the performance of image denoising. This paper presents a novel adaptive detail-preserving filter based on the cloud model (CM) to remove impulse noise. It is called the CM filter. First, an uncertainty-based detector identifies the pixels corrupted by impulse noise. Then, a weighted fuzzy mean filter is applied to remove the noise candidates. The experimental results show that, compared with the traditional switching filters, the CM filter makes a great improvement in image denoising. Even at a noise level as high as 95%, the CM filter still can restore the image with good detail preservation.

Published in:

IEEE Transactions on Image Processing  (Volume:21 ,  Issue: 7 )