access icon free Impulse noise detection technique based on fuzzy set

© The Institution of Engineering and Technology
Received 19/09/2016, Accepted 28/07/2017, Revised 01/07/2017, Published 01/08/2017

In this study, a new fuzzy-based technique is introduced for denoising images corrupted by impulse noise. The proposed method is based on the intuitionistic fuzzy set (IFS), in which the degree of hesitation plays an important role. The degree of hesitation of the pixels is obtained from the values of memberships of the object and the background of the image. After minimising the obtained hesitation function, the IFS is constructed and the noisy pixels are detected outside the neighbourhood of mean intensity of the object and the background of an image. Denoised images are relatively analysed with five other methods: modified decision-based unsymmetric trimmed median filter, noise adaptive fuzzy switched median filter, adaptive fuzzy switching weighted average filter, adaptive weighted mean filter, iterative alpha trimmed mean filter. Performances of the proposed method along with these five state-of the-art methods are evaluated using a peak signal-to-noise ratio and error rate along with the time for computation. Experimentally, derived denoising method showed an improved performance than five other existing techniques in filtering noise in images due to the reduction of uncertainty while choosing the noisy pixels.

Inspec keywords: fuzzy set theory; image denoising; iterative methods; image filtering; adaptive filters; impulse noise

Other keywords: fuzzy-based technique; intuitionistic fuzzy set; hesitation function; image denoising; modified decision-based unsymmetric trimmed median filter; impulse noise detection technique; adaptive weighted mean filter; adaptive fuzzy switching weighted average filter; IFS; iterative alpha trimmed mean filter; noise adaptive fuzzy switched median filter; signal-to-noise ratio

Subjects: Interpolation and function approximation (numerical analysis); Combinatorial mathematics; Computer vision and image processing techniques; Optical, image and video signal processing; Interpolation and function approximation (numerical analysis); Combinatorial mathematics

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