Adaptive Minimum-Maximum Exclusive Mean Filter for Impulse Noise Removal

Abstract

Many filters are proposed for impulse noise removal. However, they are hard to keep excellent denoising performance with high computational efficiency. In response to this difficulty, this paper presents a novel fast filter, adaptive minimum-maximum exclusive mean (AMMEM) filter to remove impulse noise. Although the AMMEM filter is a variety of the maximum-minimum exclusive mean (MMEM) filter, however, the AMMEM filter inherits the advantages, and overcomes the drawbacks, compared with the MMEM filter. To increase the various performances of noise removal, the AMMEM filter uses an adaptive size window, introduces two flexible factors, projection factor P and detection factor T, and limits the calculation scope of the AVG. The experimental results show the AMMEM filter makes a significant improvement in terms of noise detection, image restoration, and computational efficiency. Even at noise level as high as 95%, the AMMEM filter still can restore the images with good visual effect.