Filtering Algorithm for Image with Mixed Noises Based on Vague Sets
Bo Wei, Xiyu Wang, Zhenxi Li
Abstract
Bo Wei, Xiyu Wang, Zhenxi Li
Abstract
Combining with vague sets, a filtering algorithm was proposed to filter mixed noises in image, which was polluted by impulse and Gaussian noises. The proposed filtering algorithm was first to filter impulse noise, and then was to filter Gaussian noise. In impulse noise filter, impulse noise was detected accurately first by homogeneity histogram or homogram and the significant peak detection method of histogram. The homogram was constructed by fuzzy entropy of vague sets. Combining with an adaptive adjusting filtering window method, the detected impulse noise was removed by median filter. In Gaussian noise filter, a fuzzy weighted filter that the weight was determined by similarity measure of vague sets was adopted to reduce Gaussian noise. The experimental results show that the proposed filtering algorithm for mixed noises in images has higher resolution, and can remove mixed impulse and Gaussian noises efficiently while protecting image details.
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Combining with vague sets, a filtering algorithm was proposed to filter mixed noises in image, which was polluted by impulse and Gaussian noises. The proposed filtering algorithm was first to filter impulse noise, and then was to filter Gaussian noise. In impulse noise filter, impulse noise was detected accurately first by homogeneity histogram or homogram and the significant peak detection method of histogram. The homogram was constructed by fuzzy entropy of vague sets. Combining with an adaptive adjusting filtering window method, the detected impulse noise was removed by median filter. In Gaussian noise filter, a fuzzy weighted filter that the weight was determined by similarity measure of vague sets was adopted to reduce Gaussian noise. The experimental results show that the proposed filtering algorithm for mixed noises in images has higher resolution, and can remove mixed impulse and Gaussian noises efficiently while protecting image details.
Key concepts: Impulse noise, Gaussian noise, Median filter, Salt-and-pepper noise, Value noise, Mathematics, Gradient noise, Algorithm